Methods and systems for optimizing dietary levels utilizing artificial intelligence

ABSTRACT

A system for optimizing dietary levels utilizing artificial intelligence. The system includes at least a server designed and configured to receive at least a dietary request from a user device. The at least a server includes an alimentary instruction set generator module designed and configured to generate at least an alimentary instruction set as a function of the at least a dietary request. The at least a server includes a physical performance instruction set generator designed and configured to receive at least a provider datum, receive at least a physical performance datum, select at least a provider and at least a physical performance executor and generate at least a provider instruction set and at least a physical performance instruction set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 16/502,704, filed on Jul. 3, 2019, and entitled “METHODS AND SYSTEMSFOR OPTIMIZING DIETARY LEVELS UTILIZING ARTIFICIAL INTELLIGENCE,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for optimizing dietary levels utilizing artificialintelligence.

BACKGROUND

Historically, individuals seeking to pursue a healthier lifestyle havebeen inundated with dietary choices and options. Furthermore, decidingwhat foods to purchase and how to prepare such foods proves to be achallenge.

SUMMARY OF THE DISCLOSURE

In an aspect, a method for optimizing dietary levels utilizingartificial intelligence, the method comprising: receiving from a userclient device, at least a dietary request; selecting, by the at least aserver, a training data set from a plurality of training data sets,wherein the training data set comprises a plurality of data entriescorrelating at least a dietary request data to at least an alimentaryprocess label; generating, by the at least a server, as a function ofthe at least a dietary request and the training data set, an alimentaryinstruction set comprising at least a suggestion of items to consume bya user, wherein generating the alimentary instruction set furthercomprises: training a first machine-learning model as a function of thetraining data set and a machine-learning algorithm; and generating thealimentary instruction set as a function of the first machine-learningmodel and the at least a dietary request; identifying, by the at least aserver, at least a meal as a function of the alimentary instruction set;and selecting by the at least a server, at least a physical performanceexecutor as a function of the alimentary instruction set.

In an aspect, a system for optimizing dietary levels utilizingartificial intelligence, the system comprising: at least a server,wherein the at least a server is designed and configured to: receive,from a user client device, at least a dietary request; select a trainingdata set from a plurality of training data sets, wherein the trainingdata set comprises a plurality of data entries correlating at least adietary request data to at least an alimentary process label; generate,as a function of the at least a dietary request and the training dataset, an alimentary instruction set comprising at least a suggestion ofitems to consume by a user, wherein generating the alimentaryinstruction set further comprises: training a first machine-learningmodel as a function of the training data set and a machine-learningalgorithm; and generating the alimentary instruction set as a functionof the first machine-learning model and the at least a dietary request;identify at least a meal as a function of the alimentary instructionset; and select at least a physical performance executor as a functionof the alimentary instruction set.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for optimizing dietary levels;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of adietary data database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of analimentary instruction set generator module;

FIG. 7 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 9 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 10 is a block diagram illustrating an exemplary embodiment of analimentary instruction label classification database;

FIG. 11 is a block diagram illustrating an exemplary embodiment of analimentary instruction label learner;

FIG. 12 is a block diagram illustrating an exemplary embodiment of aphysical performance instruction set generator module;

FIG. 13 is a block diagram illustrating an exemplary embodiment of avariables database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of aprovider instruction set database;

FIG. 15 is a block diagram illustrating an exemplary embodiment aphysical performance instruction set database;

FIG. 16 is a block diagram illustrating an exemplary embodiment of aprovider network and a physical performance network;

FIG. 17 is a flow diagram illustrating an exemplary embodiment of amethod of optimizing dietary levels; and

FIG. 18 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for optimizing dietary levels utilizing artificialintelligence. In an embodiment, at least a server receives at least adietary request. The at least a dietary request is utilized by the atleast a server to generate an alimentary instruction set. In anembodiment, the at least an alimentary instruction set may be generatedutilizing training data. The at least a server receives inputs from atleast a provider datum and at least a physical performance datum. The atleast a server selects a provider and a physical performance executorutilizing a loss function and/or lazy-learning processes. The at least aserver generates at least a provider instruction set and at least aphysical performance instruction set as a function of the at least aprovider datum, the at least a physical performance datum, and the atleast an alimentary instruction set.

In an aspect, a system for optimizing dietary levels utilizingartificial intelligence, the system comprising at least a server,wherein the at least a server is designed and configured to: receive,from a user client device, at least a dietary request; select a trainingdata set from a plurality of training data sets, wherein the trainingdata set comprises a plurality of data entries correlating at least adietary request data to at least an alimentary process label; generate,as a function of the at least a dietary request and the training dataset, an alimentary instruction set comprising at least a suggestion ofitems to consume by a user, wherein generating the alimentaryinstruction set further comprises: training a first machine-learningmodel as a function of the training data set and a machine-learningalgorithm; and generating the alimentary instruction set as a functionof the first machine-learning model and the at least a dietary request;identify at least a meal as a function of the alimentary instructionset; and select at least a physical performance executor as a functionof the alimentary instruction set.

Turning now to FIG. 1, a system 100 for optimizing dietary levelsutilizing artificial intelligence is illustrated. System 100 includes atleast a server 102. At least a server 102 may include any computingdevice as described herein, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described herein. At least a server 102 may behoused with, may be incorporated in, or may incorporate one or moresensors of at least a sensor. Computing device may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. At least a server 102 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. At least a server 102 withone or more additional devices as described below in further detail viaa network interface device. Network interface device may be utilized forconnecting a at least a server 102 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. At least a server 102 mayinclude but is not limited to, for example, a at least a server 102 orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. At least aserver 102 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. At least a server 102 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. At least a server 102 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

With continued reference to FIG. 1, at least a server 102 and/or one ormore modules operating thereon may be designed and/or configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, at least a server 102 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. At least a server 102and/or one or more modules operating thereon may perform any step orsequence of steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1, server 102 is configured to receiveat least a dietary request. At least a dietary request as used in thisdisclosure includes a request for a particular diet, food, ingredient,food group, nutrition plan, style of eating, lifestyle, and/ornutrition. At least a dietary request may include a request for aparticular type of diet such as Atkins, Paleo, Whole 30, gluten free,ketogenic, dairy free, Mediterranean, soy free, and the like. At least adietary request may include elimination of certain foods or food groupsbecause of a dislike for such foods, an allergy to a food, and/or asensitivity. For example, at least a dietary request may include arequest for an egg free diet based on a user's aversion to eggs. In yetanother non-limiting example, at least a dietary request may include arequest for a diet free of bell peppers because of a user's previous IgGfood sensitivity testing. At least a dietary request may include arequest for a diet free of shellfish because of a user's IgE allergicresponse to shellfish that was diagnosed when a user was a little child.At least a dietary request may include a request for a diet based onreligious or moral beliefs such as kosher diet or vegetarian diet. Atleast a dietary request may include a request to eliminate certain foodgroups such as a nightshade free diet or a grain free diet. At least adietary request may include a request to eliminate certain ingredientsthat may be commonly found in food such as a request for a diet free ofmonosodium glutamate (MSG) or corn starch. At least a dietary requestmay include a request for a certain level or quality of ingredients suchas locally sourced ingredients, free range meats, wild caught fish,organic produce and the like. At least a dietary request may include arequest for a certain diet because of a previously diagnosed medicalcondition, such as a user who has been previously diagnosed with Candidaand is following a low sugar diet. At least a dietary request mayinclude a dietary request based on a certain style of eating that a userprefers, such as low carb, high protein, low fat, and the like. At leasta dietary request may include a dietary request as a function of amedication, supplementation, and/or medical treatment or therapy that auser may be undergoing. For example, a user currently taking amedication such as metronidazole may generate at least a dietary requestfor an alcoholic free diet, while a user currently supplementing withzinc may generate at least a dietary request free of oysters. At least adietary request may include at least a request for one meal, a specificnumber of meals such as three meals, or a certain number of meals over apredetermined time period such as a week's worth of meals. At least adietary request may include a request for specific types of meals suchas three breakfasts, two lunches, and one dinner. Meal types and mealnumbers ordered may be customized based on user inputs and user reportedeating habits. For example, a user who habitually does not eat breakfastmay not request breakfast meals, while a user who habitually eatsbreakfast at home may request a dietary request for only lunch anddinner meals.

With continued reference to FIG. 1, the at least a dietary request mayinclude at least an element of user data including a constitutionalrestriction. Element of user data as used herein, is any element of datadescribing the user, user needs, and/or user preference. At least anelement of user data may include a constitutional restriction. At leasta constitutional restriction may include any constitutional reason thata user may be unable to engage in an alimentary instruction set process;at least a constitutional restriction may include a contraindicationsuch as an injury, a previous diagnosis such as by an informed advisorincluding a functional medicine doctor, an allergy or food sensitivityissue, a contraindication due to a medication or supplement that a usermay be taking. For example, a user diagnosed with a hypercholesteremiaand currently taking a cholesterol lowering medication such as a statinmay report a constitutional restriction that includes an inability toconsume grapefruit containing foods and food products. In yet anothernon-limiting example, a user diagnosed with a shellfish allergy duringchildhood may report a constitutional restriction that includes aninability to consume shellfish or any shellfish containing foods andfood products. At least an element of user data may include at least auser preference. At least a user preference may include for examplereligious preferences such as forbidden foods, medical interventions,exercise routines and the like. For example, a user who is of Catholicfaith may report a religious preference to not consume animal productson Fridays during lent. At least a user preference may include a user'sdislike such as for example a user aversion to certain foods or nutrientgroups, such as for example an aversion to eggs or an aversion to beets.At least a user preference may include for example a user's likes suchas a user's preference to consume animal products or full fat dairy andthe like.

Continuing to refer to FIG. 1, server 102 may designed and configured toreceive training data. Training data, as used herein, is data containingcorrelation that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively, or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 1, server 102 may be configured to receive afirst training set 104 including a plurality of data entries, each dataentry of the first training set 104 including at least a dietary requestdata 106 and at least a correlated alimentary process label 108. An“alimentary process label 108,” as used in this disclosure, is anelement of data identifying a solution and/or suggestion as tonourishment requirements and/or options contained within a dietaryrequest. Alimentary process label 108 may include nourishmentrequirements and/or options including potential foods, meals,ingredients, and/or supplements that may be compatible for a user toconsume as a function of user's dietary request. For example, a dietaryrequest for a gluten free diet may contain an alimentary process label108 that contains nourishment options such as gluten free toast, glutenfree grains such as buckwheat, rice, and amaranth. In yet anothernon-limiting example, a dietary request for a raw foods diet may containan alimentary process label 108 that contains nourishment optionsincluding fruits such as strawberries, kiwis, and bananas. At least adietary request data 106 may include any data describing the user, userneeds, user dietary preferences, and/or user preferences. Dietaryrequest data 106 may include a constitutional restriction such as aninjury, a previous diagnosis from a medical professional such as afunctional medicine doctor, an allergy or food sensitivity issue, acontraindication to a medication or supplement and the like. Forexample, a user diagnosed with colitis and currently taking anantibiotic medication such as metronidazole may report a constitutionalrestriction that includes restrictions on alcohol consumption. At aleast a dietary request data 106 may include religious preferences suchas forbidden foods, medical interventions, exercise routines and thelike. At least a dietary request data 106 may include a user's dislikesuch as for example a user aversion to certain foods or nutrient groups,such as for example an aversion to liver or onions. At least a dietaryrequest data 106 may include for example a user's likes such as a user'spreference to consume animal protein or plant protein. At least adietary request data 106 may include for example, a preferred dietarystyle of eating such as vegetarian, vegan, pescatarian, flexitarian, andthe like. At least a dietary request data 106 may include a preferredstyle of eating such as for example, paleo, ketogenic, gluten free,grain free, low FODMAP, raw food diet, fruitarian, lacto vegetarianism,ovo vegetarianism, intermittent fasting, Mediterranean diet,carb-conscious, gluten free, nightshade free, dairy free, and the like.

With continued reference to FIG. 1, at least an alimentary process label108 may be correlated with at least a dietary request data 106. In anembodiment, an element of dietary request data 106 is correlated with atleast an alimentary process label 108 where the element of dietary datais located in the same data element and/or portion of data element asthe alimentary label; for example, and without limitation, an element ofdietary data is correlated with an alimentary label where both elementof dietary data and alimentary element are contained within the samefirst data element of the first training set 104. As a further example,an element of dietary data is correlated with an alimentary elementwhere both share a category label as described in further detail below,where each is within a certain distance of the other within an orderedcollection of data in data element, or the like. Still further, anelement of dietary data may be correlated with an alimentary label wherethe element of dietary data and the alimentary label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between dietary data and alimentary labels thatmay exist in first training set 104 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 1, server 102 may bedesigned and configured to associate at least an element of a dietaryrequest with a category from a list of significant categories of dietaryrequest data 106. Significant categories of dietary request data 106 mayinclude labels and/or descriptors describing types of dietary requestdata 106 that are identified as being of high relevance in identifyingalimentary process labels 108. As a non-limiting example, one or morecategories may identify significant categories of dietary request data106 based on degree of relevance to one or more impactful conditionsand/or serious adverse events associated with dietary request data. Forinstance, and without limitation, a particular set of dietary requestdata 106 that includes anaphylaxis to shellfish may be recognized asutmost importance for a user to avoid all shellfish containing foodseven those foods that may contain hidden ingredients containingshellfish derivatives such as oyster sauce as compared to dietaryrequest data 106 that includes a dislike of Brussel sprouts, wherebyingestion of Brussel sprouts may not produce an anaphylactic reactionbut rather is more indicative of a dislike. As a non-limiting example,and without limitation, dietary request data 106 describing glutenavoidance such as a gluten intolerance, Celiac Disease, wheat allergy,atopic dermatitis, fructose malabsorption, non-Celiac glutensensitivity, dermatitis herpetiformis, IgE mediated gluten allergy, IgGmediated gluten sensitivity may be recognized as useful for identifyingavoidance of various gluten containing foods and ingredients such aswheat, barley, oats, malt, croutons, corn flakes, couscous, pancakes,beer, brewer's yeast, and flour tortillas. In a further non-limitingexample, dietary request data 106 describing gluten avoidance may beuseful for identifying certain categories of foods such as grains,alcoholic beverages, sauces, dressings, baked goods, starches, and thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional categories ofphysiological data that may be used consistently with this disclosure.

Still referring to FIG. 1, server 102 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, server 102 may receive the list of significantcategories from at least an expert. In an embodiment, server 102 and/ora user device connected to server 102 may provide a first graphical userinterface 110, which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of dietary data thatthe experts consider to be significant or useful for detection ofconditions; fields in graphical user interface may provide optionsdescribing previously identified categories, which may include acomprehensive or near-comprehensive list of types of dietary datadetectable using known or recorded testing methods, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to alimentary labels, where experts may enter datadescribing alimentary labels and/or categories of alimentary labels theexperts consider related to entered categories of dietary request data;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded alimentarylabels, and which may be comprehensive, permitting each expert to selectan alimentary label and/or a plurality of alimentary labels the expertbelieves to be predicted and/or associated with each category of dietaryrequest data selected by the expert. Fields for entry of alimentarylabels and/or categories of alimentary labels may include free-form dataentry fields such as text entry fields; as described above, examinersmay enter data not presented in pre-populated data fields in thefree-form data entry fields. Alternatively or additionally, fields forentry of alimentary labels may enable an expert to select and/or enterinformation describing or linked to a category of alimentary label thatthe expert considers significant, where significance may indicate likelyimpact on longevity, mortality, quality of life, or the like asdescribed in further detail below. First graphical user interface 110may provide an expert with a field in which to indicate a reference to adocument describing significant categories of dietary data,relationships of such categories to alimentary labels, and/orsignificant categories of alimentary labels. Any data described abovemay alternatively or additionally be received from experts similarlyorganized in paper form, which may be captured and entered into data ina similar way, or in a textual form such as a portable document file(PDF) with examiner entries, or the like.

With continued reference to FIG. 1, data information describingsignificant categories of dietary request data, relationships of suchcategories to alimentary labels, and/or significant categories ofalimentary labels may alternatively or additionally be extracted fromone or more documents using a language processing module 112. Languageprocessing module 112 may include any hardware and/or software module.Language processing module 112 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 112 may compareextracted words to categories of dietary request data, one or morealimentary process labels 108, and/or one or more categories ofalimentary process labels 108 recorded at server 102; such data forcomparison may be entered on server 102 as described above using expertdata inputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively, or additionally, language processing module 112 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by server 102 and/orlanguage processing module 112 to produce associations between one ormore words extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords, and/or associations of extracted words with categories of dietarydata, relationships of such categories to alimentary labels, and/orcategories of alimentary labels. Associations between language elements,where language elements include for purposes herein extracted words,categories of dietary data, relationships of such categories toalimentary labels, and/or categories of alimentary labels may include,without limitation, mathematical associations, including withoutlimitation statistical correlations between any language element and anyother language element and/or language elements. Statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating, for instance, a likelihood that agiven extracted word indicates a given category of dietary request data,a given relationship of such categories to alimentary process labels108, and/or a given category of alimentary process labels 108. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of dietary request, a givenrelationship of such categories to alimentary process labels 108, and/ora given category of alimentary process labels 108; positive or negativeindication may include an indication that a given document is or is notindicating a category of dietary request data, relationship of suchcategory to alimentary process label 108, and/or category of alimentarylabels is or is not significant. For instance, and without limitation, anegative indication may be determined from a phrase such as “whole wheatbread was not found to be compatible with a gluten free diet,” whereas apositive indication may be determined from a phrase such as “coconutmilk was found to be compatible with a lactose free diet” as anillustrative example; whether a phrase, sentence, word, or other textualelement in a document or corpus of documents constitutes a positive ornegative indicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat server 102, or the like.

Still referring to FIG. 1, language processing module 112 and/or server102 may generate the language processing model by any suitable method,including without limitation a natural language processingclassification algorithm; language processing model may include anatural language process classification model that enumerates and/orderives statistical relationships between input term and output terms.Algorithm to generate language processing model may include a stochasticgradient descent algorithm, which may include a method that iterativelyoptimizes an objective function, such as an objective functionrepresenting a statistical estimation of relationships between terms,including relationships between input terms and output terms, in theform of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. in such models, a hidden state to be estimatedmay include an association between an extracted word category of dietarydata, a given relationship of such categories to alimentary labels,and/or a given category of alimentary labels. There may be a finitenumber of category of dietary data, a given relationship of suchcategories to alimentary labels, and/or a given category of alimentarylabels to which an extracted word may pertain; an HMM inferencealgorithm, such as the forward-backward algorithm or the Viterbialgorithm, may be used to estimate the most likely discrete state givena word or sequence of words. Language processing module 112 may combinetwo or more approaches. For instance, and without limitation,machine-learning program may use a combination of Naive-Bayes (NB),Stochastic Gradient Descent (SGD), and parameter grid-searchingclassification techniques; the result may include a classificationalgorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 112 may use acorpus of documents to generate associations between language elementsin a language processing module 112, and server 102 may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory of dietary data, a given relationship of such categories tolabels, and/or a given category of alimentary labels. In an embodiment,server 102 may perform this analysis using a selected set of significantdocuments, such as documents identified by one or more experts asrepresenting good science, good clinical analysis, or the like; expertsmay identify or enter such documents via graphical user interface asdescribed below in reference to FIG. 4, or may communicate identities ofsignificant documents according to any other suitable method ofelectronic communication, or by providing such identity to other personswho may enter such identifications into server 102. Documents may beentered into server 102 by being uploaded by an expert or other personsusing, without limitation, file transfer protocol (FTP) or othersuitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, server 102 may automatically obtain the document using such anidentifier, for instance by submitting a request to a database orcompendium of documents such as JSTOR as provided by Ithaka Harbors,Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of dietary data, a given relationship of such categoriesto alimentary labels, and/or a given category of alimentary labels isentered via graphical user interface, alternative submission means,and/or extracted from a document or body of documents as describedabove, an entry or entries may be aggregated to indicate an overalldegree of significance. For instance, each category of dietary data,relationship of such categories to alimentary labels, and/or category ofalimentary labels may be given an overall significance score; overallsignificance score may, for instance, be incremented each time an expertsubmission and/or paper indicates significance as described above.Persons skilled in the art, upon reviewing the entirety of thisdisclosure will be aware of other ways in which scores may be generatedusing a plurality of entries, including averaging, weighted averaging,normalization, and the like. Significance scores may be ranked; that is,all categories of dietary data, relationships of such categories toalimentary labels, and/or categories of alimentary labels may be rankedaccording to significance scores, for instance by ranking categories ofdietary data, relationships of such categories to alimentary labels,and/or categories of alimentary labels higher according to highersignificance scores and lower according to lower significance scores.Categories of dietary data, relationships of such categories toalimentary labels, and/or categories of alimentary labels may beeliminated from current use if they fail a threshold comparison, whichmay include a comparison of significance score to a threshold number, arequirement that significance score belong to a given portion of rankingsuch as a threshold percentile, quartile, or number of top-rankedscores.

Still referring to FIG. 1, server 102 may detect further significantcategories of dietary data, relationships of such categories toalimentary labels, and/or categories of alimentary labels usingmachine-learning processes, including without limitation unsupervisedmachine-learning processes as described in further detail below; suchnewly identified categories, as well as categories entered by experts infree-form fields as described above, may be added to pre-populated listsof categories, lists used to identify language elements for languagelearning module, and/or lists used to identify and/or score categoriesdetected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, server 102 may beconfigured, for instance as part of receiving the first training set104, to associate at least correlated first alimentary label 110 with atleast a category from a list of significant categories of alimentarylabels. Significant categories of alimentary labels may be acquired,determined, and/or ranked as described above. As a non-limiting example,alimentary labels may be organized according to relevance to and/orassociation with a list of significant foods or food groups. A list ofsignificant foods or food groups may include, without limitation, foodshaving generally acknowledged impact on dietary request. For example, adietary request such as a grain free diet may be associated with a listof significant foods such as actual grains, grain containing condimentssuch as ketchup that contains starch thickening agents, grain containingbreakfast foods such as pastries and cereals, grain containing frozenfoods, grain containing meats and the like.

With continued reference to FIG. 1, system 100 includes an alimentaryinstruction set generator module 114 operating on the at least a server.The alimentary instruction set generator module 114 may include anyhardware and/or software module as described in this disclosure.Alimentary instruction set generator module 114 is designed andconfigured to generate at least an alimentary instruction set as afunction of the at least a dietary request and the training data. In anembodiment, alimentary instruction set 116 is a data structurecontaining a solution and/or suggestion to nourishment requirements asrequested in the at least a dietary request. Alimentary instruction setmay contain suggestions as to foods and/or meals that a user may consumethat may meet requirements and/or specifications of at least a dietaryrequest. the at least a dietary request and training data. For example,at least a dietary request containing a request for a dairy free dietmay be utilized to generate an alimentary instruction set that includesa suggestion for breakfast that includes oatmeal topped with coconutmilk. In yet another non-limiting example, at least a dietary requestfor a vegetarian diet may be utilized to generate an alimentaryinstruction set that includes a meal containing tofu, spinach, and rice.In an embodiment, alimentary instruction set generator module 114 may beconfigured to modify alimentary instruction set as a function of the atleast a user entry as described in more detail below.

With continued reference to FIG. 1, alimentary instruction set module114 may include an alimentary instruction label learner 118, thealimentary instruction label learner 118 designed and configured togenerate a correlated alimentary process label 108. Alimentaryinstruction label learner 118 may include any hardware and/or softwaremodule. Alimentary instruction label learner 118 is designed andconfigured to generate outputs using machine learning processes. Amachine learning process is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” to generate analgorithm that will be performed by a computing device/module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 1, alimentary instruction label learner 118 maybe designed and configured to generate at least an alimentaryinstruction set by creating at least a first machine-learning model 120relating dietary request data 106 to alimentary labels using the firsttraining set 104 and generating the at least an alimentary instructionset using the first machine-learning model 120; at least a firstmachine-learning model 120 may include one or more models that determinea mathematical relationship between dietary request data 106 andalimentary labels. An “alimentary instruction set” as used in thisdisclosure is a data structure containing a solution and/or suggestionas to nourishment requirements and/or preferences contained within atleast a dietary request. Alimentary instruction set may include meals,foods, food groups, ingredients, supplements and the like that may becompatible with at least a dietary request. For example, alimentaryinstruction set may include a list of three possible meals that may becompatible with at least a dietary request for a dairy free diet. In yetanother non-limiting example, alimentary instruction set may includefood groups compatible with at least a dietary request such as a dietaryrequest for a paleo diet may include recommendations as to food groupsthat are compatible including meats, fish, poultry, fats, vegetables,and fruits. Machine-learning models may include without limitation modeldeveloped using linear regression models. Linear regression models mayinclude ordinary least squares regression, which aims to minimize thesquare of the difference between predicted outcomes and actual outcomesaccording to an appropriate norm for measuring such a difference (e.g. avector-space distance norm); coefficients of the resulting linearequation may be modified to improve minimization. Linear regressionmodels may include ridge regression methods, where the function to beminimized includes the least-squares function plus term multiplying thesquare of each coefficient by a scalar amount to penalize largecoefficients. Linear regression models may include least absoluteshrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

With continued reference to FIG. 1, machine-learning algorithms maygenerate alimentary instruction sets as a function of a classificationof at least an alimentary label. Classification as used herein includespairing or grouping alimentary labels as a function of a sharedcommonality. Classification may include for example, groupings,pairings, and/or trends between dietary data and current alimentarylabel, future alimentary label, and the like. In an embodiment,machine-learning algorithms may examine relationships between a futurepropensity of a user to require a new alimentary instruction set basedon current dietary data. Machine-learning algorithms may include any andall algorithms as performed by any modules, described herein foralimentary instruction label learner 118. For example, machine-learningalgorithms may relate a dietary request such as a grain free diet to auser's future propensity to require an alimentary instruction setcontaining a recommendation to consume high fiber foods.Machine-learning algorithms may examine precursor dietary requests andfuture propensity to report a subsequent dietary request. For example,machine-learning algorithms may examine a user dietary request for agluten free diet with a future propensity to report a subsequent dairyfree diet. In yet another non-limiting example, machine learningalgorithms may examine varying degrees of dietary requests andrestrictions. For example, machine-learning algorithms may examine auser dietary request for Atkins diet with a future propensity to reporta less restrictive dietary request such as the South Beach Diet. In yetanother non-limiting example, machine-learning algorithms may examine auser dietary request for a gluten free diet with a future propensity toreport a more restrictive dietary request such as a ketogenic diet.Machine-learning algorithms may examine a user dietary request forvegetarian diet with a future propensity to report a request for a vegandiet. Machine-learning algorithms may examine degree of dietaryrestriction requests and development of food allergies over time. Forexample, machine-learning algorithms may examine a user dietary requestfor an elimination diet with a future propensity to report a lessrestrictive diet as foods are reintroduced. Machine-learning algorithmsmay examine dietary requests by categories, such as demographicsincluding geographic location, age, sex, marital status, profession,income, and the like. For example, machine learning algorithms mayexamine user dietary requests in California versus user dietary requestsin Maine. Machine-learning algorithms may examine dietary requestsincluding several categories such as user dietary requests in menbetween the ages of 45-55 in Alaska versus user dietary requests amongfemales aged 18-24 in Alabama. Machine-learning algorithms may examinetrends among dietary requests generated such as for example, a dietaryrequest by a user for vegetarian options and subsequent requests by theuser for carnivore dietary requests.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 120 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, alimentary instruction label learner 118 maygenerate alimentary instruction set using alternatively or additionalartificial intelligence methods, including without limitation bycreating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing first training set 104; the trained network may then be used toapply detected relationships between elements of dietary request data106 and alimentary labels.

With continued reference to FIG. 1, machine-learning algorithms mayinclude unsupervised processes; unsupervised processes may, as anon-limiting example, be executed by an unsupervised learning moduleexecuting on server 102 and/or on another computing device incommunication with server 102, which may include any hardware orsoftware module as described as described herein. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data. Forinstance, and without limitation, alimentary instruction label learner118 and/or server 102 may perform an unsupervised machine learningprocess on first training set 104, which may cluster data of firsttraining set 104 according to detected relationships between elements ofthe first training set 104, including without limitation correlations ofelements of dietary request data 106 to each other and correlations ofalimentary labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria foralimentary instruction label learner 118 to apply in relating dietaryrequest data 106 to alimentary labels. As a non-limiting, illustrativeexample, an unsupervised process may determine that a first element ofdietary data closely with a second element of dietary data, where thefirst element has been linked via supervised learning processes to agiven alimentary label, but the second has not; for instance, the secondelement may not have been defined as an input for the supervisedlearning process, or may pertain to a domain outside of a domainlimitation for the supervised learning process. Continuing the example,a close correlation between first element of dietary request data 106and second element of dietary request data 106 may indicate that thesecond element is also a good predictor for the alimentary label; secondelement may be included in a new supervised process to derive arelationship or may be used as a synonym or proxy for the first dietarydata by alimentary label learner 114.

Still referring to FIG. 1, server 102 and/or alimentary instructionlabel learner 118 may detect further significant categories of dietarydata, relationships of such categories to alimentary labels, and/orcategories of alimentary labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, alimentaryinstruction label learner 118 and/or server 102 may continuously oriteratively perform unsupervised machine-learning processes to detectrelationships between different elements of the added and/or overalldata. Use of unsupervised learning may greatly enhance the accuracy anddetail with which system may detect alimentary labels.

With continued reference to FIG. 1, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as demographic information includingage, sex, race, geographical location, profession, and the like. Asanother non-limiting example, an unsupervised process may be performedon data concerning a particular cohort of persons; cohort may include,without limitation, a demographic group such as a group of people havinga shared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of dietary data, a group ofpeople having a shared value for an element and/or category ofalimentary label, and/or a group of people having a shared value and/orcategory of alimentary label; as illustrative examples, cohort couldinclude all people requesting a gluten free diet, all people requestinga dairy free diet, all people requesting a grain free diet, all peoplerequesting a vegetarian diet or the like. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of amultiplicity of ways in which cohorts and/or other sets of data may bedefined and/or limited for a particular unsupervised learning process.

Still referring to FIG. 1, alimentary instruction label learner 118 mayalternatively or additionally be designed and configured to generate analimentary instruction set by executing a lazy learning process as afunction of the first training set 104 and the at least a dietaryrequest; lazy learning processes may be performed by a lazy learningmodule executing on server 102 and/or on another computing device incommunication with server 102, which may include any hardware orsoftware module. A lazy-learning process and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover a “first guess” at an alimentarylabel associated with a dietary request, using first training set 104.As a non-limiting example, an initial heuristic may include a ranking ofalimentary labels according to relation to a test type of at least adietary request, one or more categories of dietary data identified intest type of at least a dietary request, and/or one or more valuesdetected in at least a dietary request; ranking may include, withoutlimitation, ranking according to significance scores of associationsbetween elements dietary data and alimentary labels, for instance ascalculated as described above. Heuristic may include selecting somenumber of highest-ranking associations and/or alimentary labels.Alimentary instruction label learner 118 may alternatively oradditionally implement any suitable “lazy learning” algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate alimentary outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Continuing to refer to FIG. 1, alimentary instruction label learner 118may generate a plurality of alimentary labels having differentimplications for a particular person. For instance, where the at least adietary request includes a request for a gluten free diet, alimentaryinstruction sets may be consistent with recommendations for mealscontaining grains such as rice, quinoa, teff, millet, buckwheat,amaranth, sorghum and the like. In such a situation, alimentaryinstruction label learner 118 and/or server 102 may perform additionalprocesses to resolve ambiguity. Processes may include presentingmultiple possible results to a user, informing the user that one or moredietary preferences are needed to determine a more definite alimentarylabel, such as a user preference for a gluten free grain of quinoa overmillet. Alternatively, or additionally, processes may include additionalmachine learning steps; for instance, where reference to a modelgenerated using supervised learning on a limited domain has producedmultiple mutually exclusive results and/or multiple results that areunlikely all to be correct, or multiple different supervised machinelearning models in different domains may have identified mutuallyexclusive results and/or multiple results that are unlikely all to becorrect. In such a situation, alimentary instruction label learner 118and/or server 102 may operate a further algorithm to determine which ofthe multiple outputs is most likely to be correct; algorithm may includeuse of an additional supervised and/or unsupervised model.Alternatively, or additionally, alimentary instruction label learner 118may perform one or more lazy learning processes using a morecomprehensive set of user data to identify a more probably correctresult of the multiple results. Results may be presented and/or retainedwith rankings, for instance to advise a user of the relativeprobabilities of various alimentary labels being correct; alternatively,or additionally, alimentary labels associated with a probability ofcorrectness below a given threshold and/or alimentary labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, a dietary request for a vegetarian diet may leadto animal containing meat products such as beef, chicken, and lamb frombeing eliminated from a list of alimentary labels for a user whilealimentary labels containing animal derived dairy products such asyogurt, cheese, and milk may be retained. Similarly, a dietary requestfor a vegan diet may eliminate all animal derived products but retainall plant sourced products including tofu, soybeans, beans, seitan,tempeh, lentils, and the like. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which additional processing may be used to determine relativelikelihoods of alimentary labels on a list of multiple alimentarylabels, and/or to eliminate some labels from such a list. Alimentaryinstruction set may be provided to user output device as described infurther detail below.

With continued reference to FIG. 1, receiving by the at least a server102 the at least a dietary request from a user device may includereceiving at least a biological extraction from a user. At least abiological extraction may include any element of physiological data.Physiological data may include any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. For instance, and without limitation,a particular set of biomarkers, test results, and/or biochemicalinformation may be recognized in a given medical field as useful foridentifying various disease conditions or prognoses within a relevantfield. As a non-limiting example, and without limitation, physiologicaldata describing red blood cells, such as red blood cell count,hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Continuing to refer to FIG. 1, physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1, physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 204 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

With continued reference to FIG. 1, physiological state data may includeone or more evaluations of sensory ability, including measures ofaudition, vision, olfaction, gustation, vestibular function and pain.Physiological state data may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data may include proteomic data, which asused herein, is data describing all proteins produced and/or modified byan organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data may include data concerning amicrobiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data of a person, and/or on prognostic labels and/orameliorative processes as described in further detail below.Physiological state data may include any physiological state data, asdescribed above, describing any multicellular organism living in or on aperson including any parasitic and/or symbiotic organisms living in oron the persons; non-limiting examples may include mites, nematodes,flatworms, or the like.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Examples of physiological state datadescribed in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

At least a biological extraction may include a physically extractedsample, which as used herein, includes a sample obtained by removing andanalyzing tissue and/or fluid. Physically extracted sample may includewithout limitation a blood sample, a tissue sample, a buccal swab, amucous sample, a stool sample, a hair sample, a fingernail sample, orthe like. Physically extracted sample may include, as a non-limitingexample, at least a blood sample. As a further non-limiting example, atleast a biological extraction may include at least a genetic sample. Atleast a genetic sample may include a complete genome of a person or anyportion thereof. At least a genetic sample may include a DNA sampleand/or an RNA sample. At least a biological extraction may include anepigenetic sample, a proteomic sample, a tissue sample, a biopsy, and/orany other physically extracted sample. At least a biological extractionmay include an endocrinal sample. As a further non-limiting example, theat least a biological extraction may include a signal from at least asensor configured to detect physiological data of a user and recordingthe at least a biological extraction as a function of the signal. Atleast a sensor may include any medical sensor and/or medical deviceconfigured to capture sensor data concerning a patient, including anyscanning, radiological and/or imaging device such as without limitationx-ray equipment, computer assisted tomography (CAT) scan equipment,positron emission tomography (PET) scan equipment, any form of magneticresonance imagery (MM) equipment, ultrasound equipment, optical scanningequipment such as photo-plethysmographic equipment, or the like. Atleast a sensor may include any electromagnetic sensor, including withoutlimitation electroencephalographic sensors, magnetoencephalographicsensors, electrocardiographic sensors, electromyographic sensors, or thelike. At least a sensor may include a temperature sensor. At least asensor may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor may be a part of system100 or may be a separate device in communication with system 100.

Still referring to FIG. 1, at least a biological extraction may includeany data suitable for use as physiological state data as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least aserver 102 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; at least a server 102 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1, at least a biological extraction mayinclude assessment and/or self-assessment data, and/or automated orother assessment results, obtained from a third-party device;third-party device may include, without limitation, a server or otherdevice (not shown) that performs automated cognitive, psychological,behavioral, personality, or other assessments. Third-party device mayinclude a device operated by an informed advisor.

Still referring to FIG. 1, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1, server 102 may be designed andconfigured to receive a second training set 122 including a plurality ofsecond data entries. Each second data entry of the second training set122 includes at least an element of biological extraction data 122; atleast an element of biological extraction data 122 may include any datadescribing a biological extraction, including any of the biologicalextractions and/or physiological data as described above. Each seconddata entry of the second training set 122 includes at least analimentary process label 108 correlated with the biological extractiondata 122, where correlation may include any correlation suitable forcorrelation of dietary request data 106 to alimentary process label 108as described above. Alimentary process label 108 may include any of thealimentary process labels 108 as described above.

With continued reference to FIG. 1, server 102 may be configured, forinstance as part of receiving second training set 122, to associatebiological extraction data 124 with at least a category from a list ofsignificant categories of biological extraction data. This may beperformed as described above for use of lists of significant categorieswith regard to dietary request data 106. Significance may be determined,and/or associated with at least a category, may be performed forbiological extraction data 122 in second training set 122 according to afirst process as described above for first training set 104. Forexample, categories may include.

Still referring to FIG. 1, server 102 may be configured, for instance aspart of receiving second training set 122, to associate at least analimentary process label 108 with at least a category from a list ofsignificant categories of alimentary process labels 108. In anembodiment, server 102 and/or a user device connected to server mayprovide a second graphical user interface 126 which may include withoutlimitation a form or other graphical element having data entry fields,wherein one or more experts, including without limitation clinicaland/or scientific experts, may enter information describing one or morecategories of biological extraction data 124 that the experts considerto be significant as described above; fields in graphical user interfacemay provide options describing previously identified categories, whichmay include a comprehensive or near-comprehensive list of types ofbiological extraction data, for instance in “drop-down” lists, whereexperts may be able to select one or more entries to indicate theirusefulness and/or significance in the opinion of the experts. Fields mayinclude free-form entry fields such as text-entry fields where an expertmay be able to type or otherwise enter text, enabling expert to proposeor suggest categories not currently recorded. Graphical user interfaceor the like may include fields corresponding to alimentary processlabels 108, where experts may enter data describing alimentary processlabels and/or categories of alimentary process labels the expertsconsider related to entered categories of biological extraction data;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded alimentarylabels, and which may be comprehensive, permitting each expert to selectan alimentary label and/or a plurality of alimentary labels the expertbelieves to be predicted and/or associated with each category ofbiological extraction data selected by the expert. Fields for entry ofalimentary labels and/or categories of alimentary labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively, oradditionally, fields for entry of alimentary labels may enable an expertto select and/or enter information describing or linked to a category ofalimentary label and/or biological extraction data that the expertconsiders significant, where significance may indicate likely impact onlongevity, mortality, quality of life, or the like as described infurther detail below. Graphical user interface may provide an expertwith a field in which to indicate a reference to a document describingsignificant categories of biological extraction data, relationships ofsuch categories to alimentary process labels, and/or significantcategories of alimentary process labels. Such information mayalternatively be entered according to any other suitable means for entryof expert data as described above. Data concerning significantcategories of biological extraction data, relationships of suchcategories to alimentary process labels, and/or significant categoriesof alimentary process labels may be entered using analysis of documentsusing language processing module 112 or the like as described above.

With continued reference to FIG. 1, alimentary instruction label learner118 may be configured to create a second machine-learning model 128relating biological extraction data 124 to alimentary process labels 108using the second training set 122 and generate alimentary instructionset 116 using the second machine-learning model 128. Secondmachine-learning model 128 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine-learning model 120.

With continued reference to FIG. 1, system 100 includes a physicalperformance instruction set generator module 130 operating on the atleast a server. The physical performance instruction set generatormodule 130 may include any hardware and/or software module as describedin this disclosure. Physical performance instruction set generatormodule 130 is designed and configured to receive at least a providerdatum, receive at least a physical performance datum, select at least aprovider and select at least a physical performance executor, andgenerate at least a provider instruction set and at least a physicalperformance instruction set as a function of the at least a providerdatum and the at least a physical performance datum and the at least analimentary instruction set.

With continued reference to FIG. 1, physical performance instruction setgenerator module 130 receives at least a provider datum 132. At least aprovider datum 132 as used herein, is any element of data describing theprovider, the provider's ability to prepare food for a certain dietaryrequest, the provider's preference to prepare food within a certaingeographical location, and/or a menu selection of food options that aprovider may be able to prepare such as a weekly menu of food options.Provider as used herein, includes any participant involved inpreparation of at least a dietary request. Provider may include arestaurant such as a local privately owned restaurant or a chainrestaurant that is located at multiple locations. Provider may include acompany that prepares pre-packaged meals. Provider may include a grocerystore that prepares meals and may include a restaurant located withinthe grocery store. Provider may include a chef or cook who preparesmeals at home or in a private commercialized kitchen. Provider mayinclude a chef or cook who prepares meals in a school or kitchen orspace that the chef or cook rents out for example. Providers may executea provider performance. A provider performance may include any actioninvolved in the preparation of and/or pursuant to at least a dietaryrequest. A provider performance may include preparation of a meal thatadheres to a dietary request from a user such as preparing a gluten freelunch for a user. A provider performance may include preparation of aweek's worth of meals for a user with a dietary request for theketogenic diet. Provider datum 132 may include for example, datadescribing a menu option for three meals a provider may be able toprepare for a user with a gluten intolerance for dinner. Provider datum132 may include for example, data describing menu options that are freeof certain allergens such as eggs, shellfish, gluten, dairy, and thelike. Provider datum 132 may include for example, a time range of howlong it may take a provider to prepare a dietary request or what hoursprovider is available to prepare a dietary request. Provider datum 132may include standards of certain ingredients that a provider may preparefoods and meals with such as locally sourced ingredients, free-rangepoultry, grass-fed meats, organic ingredients, natural ingredients, andthe like.

With continued reference to FIG. 1, physical performance instruction setgenerator module 130 receives at least a physical performance datum 134.At least a physical performance datum 134, as used herein, is anyelement of data describing the physical performance executor, thephysical performance executor's ability to deliver a dietary requestsuch as a meal based on certain constraints such as a physicalperformance executor's ability to deliver a dietary request such as ameal within a certain amount of time, the physical performanceexecutor's ability to pick up a dietary request such as a meal from aprovider within a certain geographical location, the physicalperformance executor's ability to deliver a dietary request such as ameal to a user located within a certain geographical location or thelike. Physical performance executor as used herein includes anytransportation channel that executes the physical performanceinstruction set. Physical performance executor may include an individualoperator of a mode of transportation to deliver a dietary request suchas an automobile, bicycle, scooter, boat, bus, airplane, drone,helicopter, train, and the like. Physical performance executor mayexecute a physical performance. Physical performance includes any actionthat is directed at delivering at least a dietary request. Physicalperformance executor may include an individual who works for a ridesharing company such as a taxicab service or a peer to peer ridesharingservice. Physical performance executor may include a common carrier suchas air common carriers and ground common carriers. Physical performanceexecutor may include any grocery delivery service. Physical performanceexecutor may include any food delivery service. Executing the physicalperformance instruction may include for example, picking up dietaryrequest such as a meal from a provider and delivering the dietaryrequest to a user. Physical performance instruction set may include anyof the physical performance instruction sets as described in more detailbelow. In an embodiment, physical performance executor may execute aperformance by picking up a user dietary request from a provider anddelivering the dietary request to the user. In an embodiment, physicalperformance may be segmented whereby a first physical performanceexecutor may pick up a user dietary request from a provider and deliverthe dietary request to a second physical performance executor who maydeliver the user dietary request to the user.

With continued reference to FIG. 1, physical performance instruction setgenerator module 130 may select at least a provider and at least aphysical performance executor by generating a loss function of userspecific variables and minimizing the loss function. In an embodiment,physical performance instruction set generator module 130 may compareone or more provider options and one or more physical performanceexecutor options to a mathematical expression representing an optimalcombination of user entered variables. Mathematical expression mayinclude a linear combination of variables, weighted by coefficientsrepresenting relative importance of each variable in generating anoptimal provider instruction set and an optimal physical performanceinstruction set. For instance, a variable such as total time to deliverymay be multiplied by a first coefficient representing the importance oftotal time to delivery, a second variable such as provider menu optionsmay be multiplied by a second coefficient representing the importance ofprovider menu options, a degree of variance from a delivery instructionset and/or provider instruction set may be represented as anotherparameter, which may be multiplied by an additional coefficientrepresenting an importance of that variable; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware ofdifferent variables that may be weighted by various coefficients. Use ofa linear combination is provided only as an illustrative example; othermathematical expressions may alternatively or additionally be used,including without limitation higher-order polynomial expressions or thelike.

With continued reference to FIG. 1, mathematical expression mayrepresent a loss function, where a “loss function” is an expression ofan output of which an optimization algorithm minimizes to generate anoptimal result. As a non-limiting example, physical performanceinstruction set generator module 130 may calculate variables of each ofa plurality of provider instruction sets and physical performanceinstruction sets, calculate an output of mathematical expression usingthe variables, and select a provider instruction set and physicalperformance instruction set that produces an output having the lowestsize, according to a given definition of “size” of the set of outputsrepresenting each of the plurality of provider instruction set andphysical performance instruction sets; size may, for instance, includeabsolute value, numerical size, or the like. Selection of different lossfunctions may result in identification of different provider instructionsets and physical performance instruction sets as generating minimaloutputs; for instance, where total time to delivery is associated in afirst loss function with a large coefficient or weight, a total time todelivery having a shorter time to delivery may minimize the first lossfunction, whereas a second loss function wherein total time to deliveryhas a smaller coefficient but degree of variance from provider menuoptions has a larger coefficient may produce a minimal output for adifferent provider instruction set and having a longer total time todelivery but more closely hewing to a provider menu option.

Alternatively or additionally, and still referring to FIG. 1, eachprovider instruction set and each physical performance instruction setmay be represented by a mathematical expression having the same form asmathematical expression; physical performance instruction set generatormodule 130 may compare the former to the latter using an error functionrepresenting average difference between the two mathematicalexpressions. Error function may, as a non-limiting example, becalculated using the average difference between coefficientscorresponding to each variable. Provider instruction set and physicalperformance instruction set having a mathematical expression minimizingthe error function may be selected, as representing an optimalexpression of relative importance of variables to a system or user. Inan embodiment, error function and loss function calculations may becombined; for instance, a variable resulting in a minimal aggregateexpression of error function and loss function, such as a simpleaddition, arithmetic mean, or the like of the error function with theloss function, may be selected, corresponding to an option thatminimizes total variance from optimal variables while simultaneouslyminimizing a degree of variance from a set of priorities correspondingto variables. Coefficients of mathematical expression and/or lossfunction may be scaled and/or normalized; this may permit comparisonand/or error function calculation to be performed without skewing byvaried absolute quantities of numbers.

Still referring to FIG. 1, mathematical expression and/or loss functionmay be provided by receiving one or more user commands. For instance,and without limitation, a graphical user interface may be provided touser with a set of sliders or other user inputs permitting a user toindicate relative and/or absolute importance of each variable to theuser. Sliders or other inputs may be initialized prior to user entry asequal or may be set to default values based on results of anymachine-learning processes or combinations thereof as described infurther detail below.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using a machine learning to produce lossfunction: i.e., regression. Mathematical expression and/or loss functionbe user-specific, using a training set composed of past user selections;may be updated continuously. Mathematical expression and/or lossfunction may initially be seeded using one or more user entries asabove. User may enter a new command changing mathematical expression,and then subsequent user selections may be used to generate a newtraining set to modify the new expression.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using machine learning using a multi-usertraining set. Training set may be created using data of a cohort ofpersons having similar demographic, religious, health, and/or lifestylecharacteristics to user. This may alternatively or additionally be usedto seed a mathematical expression and/or loss function for a user, whichmay be modified by further machine learning and/or regression usingsubsequent user selections of dietary requests, biological extractions,and/or alimentary process labels.

With continued reference to FIG. 1, selecting at least a provider and atleast a physical performance executor may include producing a field ofcombinations of the at least a provider and the at least a physicalperformance executor and selecting the at least a provider and the atleast a physical performance executor using a lazy-learning process.Lazy-learning process may include any of the lazy-learning process asdescribed above. Lazy-learning process may include for example,k-nearest neighbors algorithm, a lazy naive Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied. Lazy-learning process may include a continuously updatingmathematical expression such as continuously updating training sets withnew entries based on one or more user entries. User entries may updatemathematical expressions, and subsequently be utilized to generate a newtraining set to modify the new expression. In an embodiment,lazy-learning process may include performing a k-nearest neighborsalgorithm, so as to predict the classification of a new sample pointbased on already known data or training data. In an embodiment,k-nearest neighbors algorithm may assign a weighted contribution of eachneighbor, so that nearer neighbors contribute more to the average thanthe more distant ones. For example, a weighting scheme may includegiving each neighbor a weight of 1/d where d is the distance to theneighbor. The neighbor may include a set of data for which the class isknown, such as training data. In an embodiment, k-nearest neighborsalgorithm may include using training data such as vectors in amultidimensional space, each containing a class label. The training datainitially used to generate the k-nearest neighbors algorithm may includea first training set that includes the vector and correlated classlabel. In an embodiment, subsequent data may be classified during theclassification phase, whereby k is a user-defined constant based on thefirst training set and a subsequent unlabeled vector is classified byassigning a class label that is most frequent among the k trainingsamples nearest to that vector space. In an embodiment, vector space maybe measured using Euclidean distance. In an embodiment, classificationaccuracy calculations based on k values may be updated using algorithmsincluding Large Margin Nearest Neighbor and/or Neighborhood componentsanalysis. In an embodiment, neighbors may be selected using brute forcecalculated based on Euclidean distance from point of interest whoseclass label is unknown to points contained within training set. Distancemay also be measured utilizing other “norms” including for examplecosine similarity between vectors. In an embodiment, neighbors may beselected utilizing tree like data structures to determine distances frompoints of interest to points contained within training sets. In anembodiment, distances may be computed by plotting in “n-dimensional”space as defined by any suitable coordinate system including withoutlimitation Cartesian and polar, an n-dimensional vector space, or thelike, where points represent data values.

With continued reference to FIG. 1, k-nearest neighbors algorithms mayselect k values with varying values. Larger values of k may reduce theeffect of noise on classification of neighbors while making explicitboundaries between classes less distinct. K values may be calculatedutilizing heuristic techniques including hyperparameter optimization. Kvalues may be calculated utilizing bootstrapping methods.

With continued reference to FIG. 1, classification utilizing k-nearestneighbor algorithms may be useful to select optimal providers andphysical performance executors based on weighted contributions ofdatasets containing provider datums and physical performance datums.Distances between known datasets may be utilized to label subsequentdatasets including user requests for providers and physical performanceexecutors utilizing any of the methodologies as described herein. Suchcalculations may aid in selecting optimal providers and physicalperformance executors.

With continued reference to FIG. 1, physical performance instruction setgenerator module 130 is configured to generate at least a providerinstruction set including at least a provider instruction set and atleast a physical performance instruction set as a function of the atleast a provider datum and the at least a physical performance datum andthe at least an alimentary instruction set. Provider instruction set 136as used herein is a data structure containing information for providerto prepare dietary request. Provider instruction set 136 may includeuser information including remittance information, preferred remittancemethods, one or more physical addresses for a user, contact informationsuch as telephone number and email address, and any other applicableinformation relating to a user. Provider instruction set 136 may includeone or more user entries containing information such as user preferenceas to foods and ingredients contained within dietary request such as apreference for a steak to be cooked medium well or for salmon to be welldone. Provider instruction set 136 may include user entries includinginformation including a user's likes and dislikes such as a preferencefor roasted cauliflower but not boiled cauliflower. Provider instructionset 136 may include user entries including user selection of a mealand/or meals from a menu provided for by provider. For example, providerinstruction set 136 may include a specific breakfast user wants preparedsuch as oatmeal with blueberries from a menu generated by provider witha choice of five different breakfast options. Provider instruction set136 may include information contained within dietary request from a usersuch as a constitutional restriction or user preference. For example,provider instruction set 136 may include information such as aconstitutional restriction including a user's self-reported allergy toeggs or a user's inability to consume green vegetables while user istaking a blood thinning medication such as warfarin. Providerinstruction set 136 may include user input such as a user's preferencefor a certain food or meal to contain certain condiments, sauces, andsides, such as for example French fries to be delivered with ketchup ora ham and swiss sandwich to be delivered with mustard. Providerinstruction set 136 may include physical performance executorinformation including selected physical performance executor, contactinformation of physical performance executor, mode of transportation ofphysical performance executor, identification information as to physicalperformance executor such as name, or picture identification and thelike. Provider instruction set 136 may include information such as whereand how provider will hand over possession of dietary request tophysical performance executor.

With continued reference to FIG. 1, physical performance instruction set138 as used herein is a data structure containing information forphysical performance executor to deliver dietary request. Physicalperformance instruction set 138 may include any information pertainingto a user as described above in reference to provider instruction set.Physical performance instruction set 138 may include one or more userentries including for example, a user preference for dietary request toarrive at a certain time, instructions as to what physical performanceexecutor should do upon delivery such as ring user's doorbell or leavedietary request at user's doorstep. Physical performance instruction set138 may include provider information including selected provider,address of provider where physical performance executor will pick updietary request, contact information for provider such as phone numberand email. Physical performance instruction set 138 may includedirections as to how a dietary request should be handled and storedwhile under care and supervision of physical performance executor; forexample, frozen meals may need to be kept on dry ice while a freshlyprepared hamburger may need to be kept in an insulated warming tray.Physical performance instruction set 138 may include information anddirections as to where physical performance executor may meet providerto receive dietary request. For example, provider may prefer forphysical performance executor to wait in executor's mode oftransportation upon arrival at provider's kitchen for example, andprovider may walk outside to executor's mode of transportation anddeliver dietary request to executor there. In yet another non-limitingexample, provider may prefer to have executor come inside and pick updietary request in person.

With continued reference to FIG. 1, generating at least a providerinstruction set and at least a physical performance instruction set mayinclude receiving at least a user input datum. At least a user inputdatum as used herein may include any user data as it regards to at leasta dietary request. In an embodiment, at least a user input datum mayinclude a user constraint. A user constraint as used herein may includea user restriction and/or request pertaining to at least a dietaryrequest. A user constraint may include a user restriction such as acertain time of day or day of the week that user needs to receive atleast a dietary request. A user constraint may include a preference fora certain meal that at least a provider may prepare or a preference fora particular physical performance executor or for the physicalperformance executor to occur at a certain time or location. Generatingthe at least a provider instruction set and at least a physicalperformance instruction set may include receiving the at least a userconstraint, selecting at least a provider and at least a physicalperformance executor as a function of the at least a constraint, andtransmitting a subset of data associated with the at least a user to theat least a provider and the at least a physical performance executor. Inan embodiment, a provider and/or a physical performance executor may beselected who can fulfill the user constraint. For example, a user with aconstraint such as a delivery of at least a dietary request by a certaintime at night after work may then have at least a provider selected whocan prepare the user's dietary request and prepare the dietary requestwith enough time for the at least a physical performance executor todeliver the dietary request to the user by the user's predeterminedtime. At least a provider and/or at least a physical performanceexecutor who are unable to comply with user's constraint may not beselected for the user.

With continued reference to FIG. 1, system 100 includes a clientinterface module 140. Client interface module 140 may include anysuitable hardware or software module. Client interface module 176 may bedesigned and configured to transmit and receive inputs and outputs froma user client device 142. A user client device 142 may include, withoutlimitation, a display in communication with server 102; display mayinclude any display as described in this disclosure. A user clientdevice 142 may include an additional computing device, such as a mobiledevice, laptop, desktop, computer or the like. Output such as a providerinstruction set may be displayed on at least a user client device 142using for example second GUI 126.

With continued reference to FIG. 1, system 100 includes a providernetwork 144. Provider network 144 may include any provider. Provider mayinclude any of the providers as described above. Provider network 144may include at least a provider server which may include any server asdisclosed herein throughout this disclosure. The at least a providerserver may include any computing device suitable use as the at least aserver 102. Provider network 144 may include at least a providerdatabase which may include any database or datastore as disclosed inthis disclosure. Although only a single provider network 144 isdepicted, system 100 may be configured to involve multiple providernetworks or various performances within a particular provider network.Provider network 144 is described in more detail below in reference toFIG. 16.

With continued reference to FIG. 1, system 100 includes a physicalperformance entity network 146. Physical performance entity network 146may include at least a physical performance entity. Physical performanceentity may include any physical performance executor. Physicalperformance executor may include any of the physical performanceexecutors as described above, including any transportation channel thatexecutes the physical performance instruction set. Physical performanceentity network 146 may include at least a physical performance serverwhich may include any server as disclosed herein throughout thisdisclosure. The at least a physical performance server may include anycomputing device suitable for use as the at least a server 102. Physicalperformance entity network 146 may include at least a physicalperformance entity database which may include any database or datastoreas disclosed in this disclosure. Although only a single physicalperformance entity network 146 is depicted, system 100 may be configuredto involve multiple physical performance entity networks or variousperformances within a particular physical performance entity network.Physical performance entity network 146 is described in more detailbelow in reference to FIG. 16.

Referring now to FIG. 2, data incorporated in first training set 104and/or second training set 122 may be incorporated in one or moredatabases. As a non-limiting example, one or more elements of dietarydata may be stored in and/or retrieved from dietary data database. Adietary data database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A dietary data database 200 may include aplurality of data entries and/or records corresponding to elements ofdietary data as described above. Data entries and/or records maydescribe, without limitation, data concerning particular dietaryrequests that have been collected; entries may describe particular foodsand/or ingredients that are compatible with one or more dietaryrequests, which may be listed with related alimentary labels. Forexample, a dietary request for a gluten free diet and an unrelateddietary request for a Mediterranean diet may both may both be compatiblewith ingredients that include wild fish, grains such as buckwheat,polenta, and millet, and fresh vegetables such as kale, spinach, andtomatoes. Data entries may include alimentary labels and/or otherdescriptive entries describing results of evaluation of past dietaryrequests, including alimentary labels that were associated withconclusions regarding likelihood of future dietary requests associatedwith an initial dietary request. Such conclusions may have beengenerated by system 100 in previous iterations of methods, with orwithout validation of correctness by medical professionals such asfunctional medicine doctors, functional dieticians, functionalnutritionists, and the like. Data entries in a dietary data database 200may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a dietary request with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like. Additional elements of information may include oneor more categories of dietary data as described above. Additionalelements of information may include descriptions of particular methodsused to obtain dietary data, such as without limitation collectingdietary data from experts utilizing expert reports, papers, and/oropinions from experts who practice in a particular field related to aparticular dietary request. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a dietary data database 200 may reflect categories,cohorts, and/or populations of data consistently with this disclosure.

With continued reference to FIG. 2, server 102 may be configured to havea feedback mechanism. In an embodiment, server 102 may be configured toreceive a first training set 104 generated by system 100. For example,data about a user that has previously been analyzed by server 102 may beutilized in algorithms by first model 120 and/or second model 128. Suchalgorithms may be continuously updated as a function of such data. Inyet another embodiment, data analyzed by language processing module 112may be utilized as part of training data generating algorithms by firstmodel 120 and/or second model 128 and/or any other machine learningprocess performed by server 102.

Referring now to FIG. 3, an exemplary embodiment of dietary datadatabase 200 is illustrated. One or more database tables in dietary datadatabase 200 may include, as a non-limiting example, a compatible foodstable 300. For instance, and without limitation, compatible foods table300 may be a table relating dietary requests to foods that arecompatible with a particular dietary request; for instance where adietary request contains a request for a ketogenic diet, foods such asbeef tips, ground sirloin and lamb shanks may be compatible with such arequest while such foods may not be compatible with a dietary requestfor a vegan diet. Dietary data database 200 may include moderatelycompatible food table 304 which may be a table relating dietary requestto foods that are moderately compatible with a particular dietaryrequest; for instance where a dietary request contains a request for agluten free diet from a user with a self-reported gluten intolerance,foods such as certified gluten free oats may be moderately compatiblewith such a user, while certified gluten free oats may not be compatiblefor a user following a gluten free diet because of a previous diagnosisof Celiac Disease. For instance, and without limitation, dietary datadatabase 200 may include as a non-limiting example, incompatible foodtable 308. For instance, and without limitation, incompatible food table308 may include a table relating dietary requests to foods that areincompatible with a particular dietary request; for instance where adietary request contains a request for a corn free diet ingredients suchas cornstarch, corn oil, dextrin, maltodextrin, dextrose, fructose,ethanol, maize, and/or sorbitol may be listed. In an embodiment,database tables contained within dietary data database 200 may includegroupings of foods by different categories such as grains, meats,vegetables, fruits, sugars and fats, and the like. In an embodiment,database tables contained within dietary data database 200 may includegroups of foods by ingredients that a food may be comprised of, forexample gravy may contain flour which may contain gluten.

Referring again to FIG. 2, server 102 and/or another device in system100 may populate one or more fields in dietary data database 200 usingexpert information, which may be extracted or retrieved from an expertknowledge database 204. An expert knowledge database 204 may include anydata structure and/or data store suitable for use as dietary datadatabase 200 as described above. Expert knowledge database 204 mayinclude data entries reflecting one or more expert submissions of datasuch as may have been submitted according to any process described abovein reference to FIG. 1, including without limitation by using firstgraphical user interface 110 and/or second graphical user interface 126.Expert knowledge database may include one or more fields generated bylanguage processing module 112, such as without limitation fieldsextracted from one or more documents as described above. For instance,and without limitation, one or more categories of dietary data and/orrelated alimentary labels and/or categories of alimentary labelsassociated with an element of physiological state data as describedabove may be stored in generalized from in an expert knowledge database204 and linked to, entered in, or associated with entries in a dietarydata database 200. Documents may be stored and/or retrieved by server102 and/or language processing module 112 in and/or from a documentdatabase 208; document database 208 may include any data structureand/or data store suitable for use as dietary data database 200 asdescribed above. Documents in document database 208 may be linked toand/or retrieved using document identifiers such as URI and/or URL data,citation data, or the like; persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdocuments may be indexed and retrieved according to citation, subjectmatter, author, date, or the like as consistent with this disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 204 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertdietary table 400. Expert dietary table 400 may be a table relatingdietary data as described above to alimentary labels; for instance,where an expert has entered data relating an alimentary label to acategory of dietary data and/or to an element of dietary data via firstgraphical user interface 110 as described above, one or more rowsrecording such an entry may be inserted in expert dietary table 400. Inan embodiment, a forms processing module 404 may sort data entered in asubmission via first graphical user interface 110 by, for instance,sorting data from entries in the first graphical user interface 110 torelated categories of data; for instance, data entered in an entryrelating in the first graphical user interface 110 to an alimentarylabel may be sorted into variables and/or data structures for storage ofalimentary labels, while data entered in an entry relating to a categoryof dietary data and/or an element thereof may be sorted into variablesand/or data structures for the storage of, respectively, categories ofdietary data or elements of dietary data. Where data is chosen by anexpert from pre-selected entries such as drop-down lists, data may bestored directly; where data is entered in textual form, languageprocessing module 112 may be used to map data to an appropriate existinglabel, for instance using a vector similarity test or othersynonym-sensitive language processing test to map dietary data to anexisting label. Alternatively or additionally, when a languageprocessing algorithm, such as vector similarity comparison, indicatesthat an entry is not a synonym of an existing label, language processingmodule may indicate that entry should be treated as relating to a newlabel; this may be determined by, e.g., comparison to a threshold numberof cosine similarity and/or other geometric measures of vectorsimilarity of the entered text to a nearest existent label, anddetermination that a degree of similarity falls below the thresholdnumber and/or a degree of dissimilarity falls above the thresholdnumber. Data from expert textual submissions 408, such as accomplishedby filling out a paper or PDF form and/or submitting narrativeinformation, may likewise be processed using language processing module112. Data may be extracted from expert papers 412, which may includewithout limitation publications in medical and/or scientific journals,by language processing module 112 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure. Expert dietary table 400may include a single table and/or a plurality of tables; plurality oftables may include tables for particular categories of prognostic labelssuch as a current diagnosis table, a future prognosis table, a genetictendency table, a metabolic tendency table, and/or an endocrinaltendency table (not shown), to name a few non-limiting examplespresented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more alimentary process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from firstgraphical user interface 110 via forms processing module 404 and/orlanguage processing module 112, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an alimentary nutrition table 416 may list one or morealimentary recommendations based on nutritional instructions, and/orlinks of such one or more alimentary recommendations to alimentarylabels, as provided by experts according to any method of processingand/or entering expert data as described above. As a further example analimentary fulfillment action table 420 may list one or more alimentaryprocesses based on instructions for fulfillment actions a user shouldtake, including without limitation fulfillment actions such aspurchasing groceries at a grocery store, ordering groceries online,ordering a meal at a restaurant, cooking a meal at home, ordering a mealdelivery kit, cooking a meal delivery kit, hiring a chef to preparemeals, and/or links of such one or more dietary requests to alimentarylabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, an alimentary supplement table 428 may list one or morealimentary processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more dietary requests toalimentary labels, as provided by experts according to any method ofprocessing and/or entering expert data as described above. Alimentarysupplement table 428 may list a recommended supplement a user mayconsider taking as a function of a dietary request. For example, adietary request such as a vegan diet may be recommended to supplementwith B vitamins. As a further non-limiting example, an alimentarymedication table 424 may list one or more alimentary processes based onmedications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moredietary requests to alimentary labels, as provided by experts accordingto any method of processing and/or entering expert data as describedabove. Alimentary medication table 424 may recommend a dietary requestas a function of a medication a user may be taking. For example, a usertaking an antibiotic such as metronidazole may be recommended toeliminate alcohol, while a user taking a medication such as doxycyclinemay be recommended to eliminate dairy containing products. As anadditional example, a counterindication table 432 may list one or morecounter-indications for one or more dietary requests; counterindicationsmay include, without limitation allergies to one or more foods,medications, and/or supplements, side-effects of one or more medicationsand/or supplements, interactions between medications, foods, and/orsupplements, exercises that should not be used given one or more dietaryrequest.

Referring now to FIG. 2, system 100 may include or communicate with analimentary process label database 212; an alimentary process labeldatabase 212 may include any data structure and/or datastore suitablefor use as a dietary data database 200 as described above. An alimentaryprocess label database 212 may include one or more entries listinglabels associated with one or more alimentary processes as describedabove, including any dietary requests correlated with alimentary labelsin first training set 104 as described above; alimentary process labelsmay be linked to or refer to entries in alimentary label database 212 towhich alimentary process labels correspond. Linking may be performed byreference to historical data concerning alimentary labels, such asingredients, products, food items, lifestyle, and/or dietary choicesassociated with dietary requests in the past; alternatively oradditionally, a relationship between an alimentary process label and adata entry in alimentary process label database 212 may be determined byreference to a record in an expert knowledge database 204 linking agiven alimentary process label to a given category of alimentary labelas described above. Entries in alimentary process label database 212 maybe associated with one or more categories of alimentary labels asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 204.

With continued reference to FIG. 2, first training set 104 may bepopulated by retrieval of one or more records from dietary data database200 and/or alimentary process label database 212; in an embodiment,entries retrieved from dietary data database 200 and/or alimentaryprocess label database 212 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a first training set 104 including data belonging to agiven cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom system 100 classifies dietary requests to alimentarylabels as set forth in further detail below. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which records may be retrieved from dietary data database 200and/or alimentary process label database 212 to generate a training setto reflect individualized group data pertaining to a person of interestin operation of system and/or method, including without limitation aperson with regard to whom at least a dietary request is beingevaluated. Server may alternatively or additionally receive a firsttraining set 104 and store one or more entries in dietary database 200and/or alimentary process label database 212 as extracted from elementsof first training set 104.

With continued reference to FIG. 2, first training set 104 may bepopulated by matching user entries with dietary requests. For example,first training set 104 may be populated by analyzing user entries suchas by language processing module 112 to analyze what types of mealsand/or food choices that a user made. User entries as described in moredetail below, may be received by server 102 such as from user clientdevice 142. In an embodiment, user may generate user entries at firstGUI 110, second GUI 126, and/or client interface module 140. Userentries may then be matched against an associated dietary request. Forexample, a user entry for a dietary request such as a vegan diet may beanalyzed by language processing module 112 to determine what previousproviders were able to be selected that could fulfill user's request. Inyet another non-limiting example, a user entry for a dietary requestsuch as a gluten free diet may be analyzed by language processing module112 to determine alimentary process label 108 that dietary input may bematched with. Such data may then be utilized as first training set 104.First training set 104 may also be obtained by performing a lossfunction and optimizing roots as described in more detail below.

With continued reference to FIG. 2, server 102 may receive an update toone or more elements of data represented in first training set 104 andmay perform one or more modifications to first training set 104, or todietary data database 200, expert knowledge database 204, and/oralimentary process label database 212 as a result. For instance, adietary request may turn out to have been erroneously recorded such aswhen a user requested a dietary request but mere seconds later revokedsuch a request; server 102 may remove it from first training set 104,dietary data database 200, expert knowledge database 204, and/oralimentary process label database 212 as a result. As a further example,a medical and/or academic paper, or a study on which it was based, maybe revoked; server 102 may remove it from first training set 104,dietary data database 200, expert knowledge database 204, and/oralimentary process label database 212 as a result. Information providedby an expert may likewise be removed if the expert loses credentials oris revealed to have acted fraudulently.

Continuing to refer to FIG. 2, elements of data first training set 104,dietary database 200, expert knowledge database 204, and/or alimentaryprocess label database 212 may have temporal attributes, such astimestamps; server 102 may order such elements according to recency,select only elements more recently entered for first training set 104and/or otherwise bias training sets, database entries, and/ormachine-learning models as described in further detail below toward morerecent or less recent entries. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which temporal attributes of data entries may be used to affectresults of methods and/or systems as described herein.

With continued reference to FIG. 2, data incorporated in second trainingset 122 may be incorporated in one or more databases. As a non-limitingexample, one or more elements of biological extraction data may bestored in and/or retrieved from a biological extraction database 216.Biological extraction database 216 may include any database or datastorestructure as described herein.

Referring now to FIG. 5, an exemplary embodiment of biologicalextraction database 216 is illustrated. Biological extraction database216 may include as a non-limiting example, prognostic link table 500.Prognostic link table 500 may be a table relating biological extractiondata to a prognostic label; for instance, where an expert has entereddata relating a prognostic label to a category of biological extractiondata, such information may be contained within prognostic link table500. Biological extraction database 216 may include a fluid sample table504 listing samples acquired from a person by extraction of fluids, suchas without limitation, blood, lymph cerebrospinal fluid, or the like. Asanother non-limiting example, biological extraction database 216 mayinclude a sensor data table 508, which may list samples acquired usingone or more sensors, for instance as described in further detail below.As a further non-limiting example, biological extraction database 216may include a genetic sample table 512, which may list partial or entiresequences of genetic material. Genetic material may be extracted andamplified, as a non-limiting example, using polymerase chain reactions(PCR) or the like. As a further example, also non-limiting, biologicalextraction database 216 may include a medical report table 516, whichmay list textual descriptions of medical tests, including withoutlimitation radiological tests or tests of strength and/or dexterity orthe like. Data in medical report table may be sorted and/or categorizedusing language processing module 112, for instance, translating atextual description into a numerical value and a label corresponding toa category of biological extraction data; this may be performed usingany language processing algorithm or algorithms as described in thisdisclosure. As another non-limiting example, biological extractiondatabase 216 may include a tissue sample table 520, which may recordbiological extraction data obtained using tissue samples. Tablespresented above are presented for exemplary purposes only; personsskilled in the art will be aware of various ways in which data may beorganized in biological extraction database 216 consistently with thisdisclosure.

Referring now to FIG. 6, an exemplary embodiment of alimentaryinstruction set generator module is illustrated. Alimentary instructionset generator module 114 is designed and configured to generate at leastan alimentary instruction set as a function of the at least a dietaryrequest. Alimentary instruction set may include any of the alimentaryinstruction sets as described above In an embodiment, alimentaryinstruction set generator module 114 may generate alimentary instructionset 116 based on integration of data associated with first training set104, second training set 122 any applicable external sources, and anyapplicable database within system 100. Generation of alimentaryinstruction set 116 may include identification of one or more alimentaryinstruction sets as a function of dietary request including for examplea user input datum such as a constitutional restriction, userpreference, and the like. Generation of alimentary instruction set 116may include identification of one or more alimentary instruction sets asa function of biological extraction data. Generation of alimentaryinstruction set 116 may include identification of one or more alimentaryinstructions and insertion of the one or more alimentary instructions inthe alimentary instruction set 116. For example, alimentary instructionset 116 may be formed, wholly or partially, by aggregating alimentaryinstructions and combining the aggregated alimentary instructions usingnarrative language module, narrative language database, image database,or the like as described in more detail below.

With continued reference to FIG. 6, alimentary instruction set generatormodule 114 may include a label synthesizer 604. Label synthesizer 604may include any suitable software or hardware module. In an embodiment,label synthesizer 604 may be designed and configured to combine aplurality of labels in at least an alimentary process label together toprovide maximally efficient data presentation. Combination of labelstogether may include elimination of duplicate information. For instance,label synthesizer 604 and/or at least a server 102 may be designed andconfigure to determine a first alimentary label of the at least analimentary label is a duplicate of a second alimentary label of the atleast an alimentary label and eliminate the first alimentary label.Determination that a first alimentary label is a duplicate of a secondalimentary label may include determining that the first alimentary labelis identical to the second alimentary label; for instance, an alimentarylabel generated from test data presented in one dietary request of atleast a dietary request may be the same as an alimentary label generatedfrom test data presented in a second dietary request of at least adietary request. As a further non-limiting example, a first alimentarylabel may be synonymous with a second alimentary label, where detectionof synonymous labels may be performed, without limitation, by a languageprocessing module 112 as described above.

Continuing to refer to FIG. 6, label synthesizer 604 may groupalimentary labels according to one or more classification systemsrelating the alimentary labels to each other. For instance, alimentaryinstruction set generator module 114 and/or label synthesizer 604 may beconfigured to determine that a first alimentary label of the at least analimentary label and a second alimentary label of the at least analimentary label belong to a shared category. A shared category may bean ingredient, food and/or or category of food or ingredient to whicheach of first alimentary label and second alimentary label belongs; asan example, lactose free diet and dairy free diet may be examples ofdietary requests which may in turn share of a category of foodingredients such as milk alternatives including coconut milk, almondmilk, hemp milk, oat milk, and/or soy milk.

With continued reference to FIG. 6, alimentary data may be identifiedand aggregated into a subset of applicable alimentary data based on atleast a dietary request and first training set 104. Alimentary data maybe identified and aggregated into a subset of applicable alimentary databased on at least a biological extraction data and second training set122. In an embodiment, alimentary instruction set 116 may comprise aplurality of alimentary data specific to user that is able to be used bymachine learning and artificial intelligence systems in order tocontinuously update or modify training sets, and alimentary instructionset 116 based on updated or progressions associated with implementationof alimentary instruction set 116 by user. Alimentary data andnon-alimentary data may include compilations of instruction setsreceived over a period of time, the compilations may account forimprovements or modifications associated with user. Alimentaryinstruction set 116 may further include instructions over time, in whichthe alimentary instructions may change in response to changes in auser's data and/or prognosis. Alternatively or additionally, system 100may periodically iterate through one or more processes as described inthis disclosure, such that repeated reevaluations may modify alimentaryinstruction set 116 as information concerning user and/or dietaryrequests obtained from the user change over time.

With continued reference to FIG. 6, in one embodiment, alimentaryinstruction set generator module 114 may be configured to generatealimentary instruction set process descriptor 616 by converting one ormore alimentary instruction set labels into narrative language. As anon-limiting example, alimentary instruction set generator module 114may include and/or communicate with narrative language unit 608, whichmay be configured to determine an element of narrative languageassociated with at least an alimentary instruction set label and includethe element of narrative language in current alimentary instruction setlabel descriptor. Narrative language unit 608 may implement this,without limitation, by using a language processing module 112 to detectone or more associations between alimentary instruction set labels, orlists of alimentary instruction set labels, and phrases and/orstatements of narrative language. Alternatively or additionally,narrative language unit 608 may retrieve one or more elements ofnarrative language from narrative language database 612, which maycontain one or more tables associating alimentary instruction set labelsand/or groups of alimentary instruction set labels with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in alimentary instruction set, forinstance for display to a user as text describing a current alimentaryinstruction set status of the user. Alimentary instruction set processdescriptor 616 may further include one or more images; one or moreimages may be retrieved by alimentary instruction set generator modulefrom an image database 620, which may contain one or more tablesassociating alimentary instruction set labels, groups of alimentaryinstruction set labels, alimentary instruction set process descriptors616, or the like with one or more images.

With continued reference to FIG. 6, in an embodiment, relationshipsbetween alimentary labels and categories may be retrieved from analimentary instruction label classification database 628, for instanceby generating a query using one or more alimentary labels of at least analimentary output, entering the query, and receiving one or morecategories matching the query from the alimentary instruction labelclassification database 628.

Referring now to FIG. 7, an exemplary embodiment of narrative languagedatabase 612 is illustrated. Narrative language database 612 may beimplemented as any database and/or datastore suitable for use asbiological extraction database 216 as described above. One or moredatabase tables in narrative language database 612 may include, withoutlimitation, an alimentary description table 700, which may linkalimentary labels to narrative descriptions associated with alimentarylabels. One or more database tables in narrative language database 612may include, without limitation, a dietary request table 704, which maylink dietary requests to narrative descriptions associated withalimentary process labels. One or more database tables in narrativelanguage database 612 may include, without limitation, a biologicalextraction table 708, which may link biological extractions to narrativedescriptions associated with alimentary process labels. One or moredatabase tables in narrative language database 612 may include, withoutlimitation, a combined description table 712, which may linkcombinations of dietary requests and alimentary labels as well asbiological extractions and alimentary labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 612 may include, without limitation, aparagraph template table 716, which may contain one or more templates ofparagraphs, pages, reports, or the like into which images and text, suchas images obtained from image database 620 and text obtained fromalimentary description table 700, dietary request table 704, biologicalextraction table 708, and combined description table 712 may beinserted. Tables in narrative language database 612 may be populated, asa non-limiting example, using submissions from experts, which may becollected according to any processes described above. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various way sin which entries in narrative language database 612 maybe categorized and/or organized.

Referring now to FIG. 8, an exemplary embodiment of image database 620is illustrated. Image database 620 may be implemented as any databaseand/or datastore suitable for use as dietary database 200 as describedabove. One or more database tables in image database 620 may include,without limitation, a dietary request image table 800, which may linkdietary requests to images associated with alimentary labels. One ormore database tables in image database 620 may include, withoutlimitation, a biological extraction image table 804, which may linkbiological extractions to images associated with alimentary processlabels. One or more database tables in image database 620 may include,without limitation, an alimentary image table 808, which may linkalimentary process labels to images associated with alimentary processlabels. One or more database tables in image database 620 may include,without limitation, a combined description table 812, which may linkcombinations of dietary requests, biological extractions and alimentarylabels to images associated with the combinations. One or more databasetables in image database 620 may include, without limitation, a dietaryrequest video table 816, which may link dietary requests to videosassociated with alimentary labels. One or more database tables in imagedatabase 620 may include, without limitation, a biological extractionvideo table 820, which may link biological extractions to videosassociated with alimentary process labels. One or more database tablesin image database 620 may include, without limitation, an alimentaryvideo table 824, which may link alimentary process labels to videosassociated with alimentary process labels. One or more database tablesin image database 620 may include, without limitation, a combined videotable 828, which may link combinations of dietary requests, biologicalextractions and alimentary labels to videos associated with thecombinations. Tables in image database 620 may be populated, withoutlimitation, by submissions by experts, which may be provided accordingto any process or process steps described in this disclosure forcollection of expert submissions.

Referring now to FIG. 9, an exemplary embodiment of user database 624 isillustrated. User database 624 may be implemented as any database and/ordatastore suitable for use as described above. One or more databasetables in user database 624 may include, without limitation, aconstitutional restriction table 900; at least a constitutionalrestriction may be linked to a given user and/or user identifiercontained within a constitutional restriction table 900. One or moredatabase tables in user database 624 may include, without limitation, auser preference table 904; at least a user preference may be linked to agiven user and/or user identifier in a user preference table 904.

Referring now to FIG. 10, an exemplary embodiment of an alimentaryinstruction label classification database 628 is illustrated. Alimentaryinstruction label classification database 628 may operate on the server102. Alimentary instruction label classification database 628 may beimplemented as any database and/or datastore suitable for use as adatabase. One or more database tables in alimentary instruction labelclassification database 628 may include, without limitation, analimentary category table 1000; which may associate an alimentaryinstruction label with one or more categories of nutritional properties,ingredients, foodstuffs, or the like. One or more database tables inalimentary instruction label classification database 628 may include,without limitation, an nutrition category table 1004, which may describeone or more categories of nutrition, such as breakdown by fats,carbohydrates, protein, vegetables, fruits, and the like or nutritioncategories such as breakdown by micronutrient such as calcium, VitaminA, Vitamin D, iron, chromium and the like. One or more database tablesin alimentary instruction label classification database 628 may include,without limitation, a supplement table 1008, which may describe asupplement that relates to a dietary request, such as a grain free dietwith a recommendation for fiber supplementation or a vegetarian dietwith a recommendation for B vitamin supplementation.

Referring now to FIG. 11, an exemplary embodiment of alimentaryinstruction label learner 118 is illustrated. Alimentary instructionlabel learner 118 may be configured to perform one or more supervisedlearning processes, supervised learning processes may be performed by asupervised learning module 1104 executing on server 102 and/or onanother computing device in communication with server 102, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of dietary data as inputs, alimentary labels as outputs, and ascoring function representing a desired form of relationship to bedetected between elements of dietary data and alimentary labels; scoringfunction may, for instance, seek to maximize the probability that agiven element of dietary data and/or combination of elements of dietarydata is associated with a given alimentary label and/or combination ofalimentary labels to minimize the probability that a given element ofdietary data and/or combination of elements of dietary data is notassociated with a given alimentary label and/or combination ofalimentary labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set104. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of dietary data and alimentary labels. In yet anothernon-limiting example, a supervised learning algorithm may use elementsof biological extraction data as inputs, alimentary labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of biological extraction data and alimentarylabels. For example, an input containing a biological extraction such asan elevated fasting glucose blood level may be related to an alimentarylabel that includes impaired carbohydrate metabolism. In an embodiment,one or more supervised machine-learning algorithms may be restricted toa particular domain for instance, a supervised machine-learning processmay be performed with respect to a given set of parameters and/orcategories of parameters that have been suspected to be related to agiven set of alimentary labels, and/or are specified as linked to aparticular field of dietary requests. As a non-limiting example, aparticular set of foods and/or food groups may be typically consumed bycertain diets such as for example, coconut meat consumed on a ketogenicdiets or raw foods diet, and a supervised machine-learning process maybe performed to relate those foods and/or food groups to the variousdietary requests; in an embodiment, domain restrictions of supervisedmachine-learning procedures may improve accuracy of resulting models byignoring artifacts in training data. Domain restrictions may besuggested by experts and/or deduced from known purposes for particularevaluations and/or known tests used to evaluate alimentary labels.Additional supervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between dietary data and alimentary labels.

With continued reference to FIG. 11, alimentary instruction labellearner 118 may perform one or more unsupervised machine-learningprocesses as described above; unsupervised processes may be performed byan unsupervised learning module 1108 executing on server 102 and/or onanother computing device in communication with server 102, which mayinclude any hardware or software module. For instance, and withoutlimitation, alimentary instruction label learner 118 and/or server 102may perform an unsupervised machine learning process on first trainingset 104, which may cluster data of first training set 104 according todetected relationships between elements of the first training set 104,including without limitation correlations of alimentary labels to eachother; such relations may then be combined with supervised machinelearning results to add new criteria for alimentary instruction labellearner 118 to apply in relating dietary data to alimentary labels. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first dietary request correlates closely with a seconddietary request, where the first dietary request has been linked viasupervised learning processes to a given alimentary label, but thesecond has not; for instance, the second dietary request may not havebeen defined as an input for the supervised learning process, or maypertain to a domain outside of a domain limitation for the supervisedlearning process. Continuing the example, a close correlation betweenfirst dietary request and second dietary request may indicate that thesecond dietary request is also a good match for the alimentary label;second dietary request may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstdietary request by alimentary instruction label learner 118.Unsupervised processes performed by alimentary instruction label learner118 may be subjected to any domain limitations suitable for unsupervisedprocesses as described above.

Still referring to FIG. 11, server 102 and/or alimentary instructionlabel learner 118 may detect further significant categories of dietaryrequests, relationships of such categories to alimentary labels, and/orcategories of alimentary labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to server 102, alimentaryinstruction label learner 118 and/or server 102 may continuously oriteratively perform unsupervised machine-learning processes to detectrelationships between different elements of the added and/or overalldata; in an embodiment, this may enable server 102 to use detectedrelationships to discover new correlations between known dietaryrequests, alimentary labels, and one or more elements of data in largebodies of data, such as nutritional, health, lifestyle, and/ordietary-related data, enabling future supervised learning and/or lazylearning processes to identify relationships between, e.g., particulardietary requests and particular alimentary labels. In an embodiment, useof unsupervised learning may greatly enhance the accuracy and detailwith which system may detect alimentary labels.

Continuing to view FIG. 11, alimentary instruction label learner 118 maybe configured to perform a lazy learning process as a function of thefirst training set 104 and the at least a dietary request to produce theat least an alimentary output; a lazy learning process may include anylazy learning process. Lazy learning processes may be performed by alazy learning module 1112 executing on server 102 and/or on anothercomputing device in communication with server 102, which may include anyhardware or software module.

With continued reference to FIG. 11, alimentary instruction labellearner 118 may generate a plurality of alimentary labels havingdifferent implications for a particular person. For instance, where adietary request includes a request for a grain free diet, variousdietary choices may be generated as alimentary labels associated withthe dietary request, such as alimentary labels that may include proteinchoices such as lamb, veal, beef, chicken, cod, salmon, shrimp, andherring. In such an instance, alimentary instruction label learner 118and/or server 102 may perform additional processes to resolve ambiguity.Processes may include presenting multiple possible results to a user,informing the user of various options that may be available, and/or thatfollow-up question may be required to select an appropriate choice suchas asking a user what protein choices user prefers, likes, and/ordislikes. Alternatively, or additionally, processes may includeadditional machine learning steps. For instance, alimentary instructionlabel learner 118 may perform one or more lazy learning processes usinga more comprehensive set of user data to identify a more probablycorrect result of the multiple results. Alimentary instruction labellearner 118 may generate alimentary data output 1116 as a function offirst training set 104 and/or first model 116. Results may be presentedand/or retained with rankings, for instance to advise a user of therelative probabilities of various alimentary labels being correct orideal choices for a given user; alternatively, or additionally,alimentary labels associated with a probability of success orsuitability below a given threshold and/or alimentary labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an additional process may reveal that a user isallergic to salmon, and consumption of salmon may be eliminated as analimentary label to be presented.

Continuing to refer to FIG. 11, alimentary instruction label learner 118may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 1120. As used herein,longitudinal data 1120 may include a temporally ordered series of dataconcerning the same user, or the same cohort of users; for instance,longitudinal data 1120 may describe a series of alimentary instructionsets generated for a user over a period of time such as over the courseof a month or year. Longitudinal data 1120 may relate to a series ofsamples tracking response of one or more elements of dietary datarecorded regarding a person undergoing one or more alimentary processeslinked to one or more alimentary process labels. Alimentary instructionlabel learner 118 may track one or more elements of dietary data andfit, for instance, a linear, polynomial, and/or splined function to datapoints; linear, polynomial, or other regression across larger sets oflongitudinal data, using, for instance, any regression process asdescribed above, may be used to determine a best-fit graph or functionfor the effect of a given alimentary process over time on a dietaryrequest. Functions may be compared to each other to rank alimentaryprocesses; for instance, an alimentary process associated with a steeperslope in curve representing improvement in a dietary request, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an alimentary process associated with a less steep slope foran improvement curve or a steeper slope for a curve marking a decline.Alimentary processes associated with a curve and/or terminal data pointrepresenting a value that does not associate with a previously detectedalimentary process label may be ranked higher than one that is not soassociated. Information obtained by analysis of longitudinal data 1120may be added to alimentary process database and/or training set.

With continued reference to FIG. 11, alimentary instruction labellearner 118 may utilize past user entries 1124 to continuously updatefirst model 120 and/or second model 128. Past user entries 1124 mayinclude previous dietary requests including for example constitutionalrestrictions, and user preferences, as well as previous biologicalextractions. Such information may be continuously supplied to alimentaryinstruction label leaner 118 to provide real-time data to generate moreaccurate algorithms and models.

Referring now to FIG. 12, an exemplary embodiment of physicalperformance instruction set generator module 130 is illustrated.Physical performance instruction set generator module 130 may includeany hardware or software module as described above. Physical performanceinstruction set generator module 130 is designed and configured toreceive at least a provider datum, receive at least a physicalperformance datum, select at least a provider and at least a physicalperformance executor, and generate at least a provider instruction setand at least a physical performance instruction set as a function of theat least a provider datum and the at least a physical performance datumand the at least an alimentary instruction set.

With continued reference to FIG. 12, selecting at least a provider andat least a physical performance executor may be performed by physicalperformance instruction set generator module 130 by generating a lossfunction of user specific variables and minimizing the loss function.Physical performance instruction set generator module 130 may utilizevariables database 1200 to generate a loss function using differentvariables and minimize the loss function. Variables that may be utilizedand stored within variables database 1200 are described in more detailbelow in reference to FIG. 13. Loss function may include any of the lossfunctions as described above in reference to FIG. 1.

With continued reference to FIG. 12, selecting at least a provider andat least a physical performance executor may be performed by physicalperformance instruction set generator module 130 by utilizing lazylearning methods such as by lazy learning module 1112. Lazy learningmethods may include producing a field of the at least a provider and theat least a physical performance executor combination and selecting atthe at least a provider and the at least a physical performance executorusing a lazy-learning process. Lazy-learning process may includegenerating algorithms including k-nearest neighbors function. K-nearestneighbors function may include any of the k-nearest neighbors algorithmsas described above in reference to FIG. 1. For example, in anembodiment, physical performance instruction set generator module 130may receive data based on user inputs, provider datums 132, and/orphysical performance datums 134 in regards to a dietary request receivedfrom a user client device 142. This may include for example, informationpertaining to providers and/or physical performance executors who canaid in fulfilling user's dietary request. For example, a provider datums132 may contain information such as contact information for providers aswell as possible menu options for those who can fulfill and prepare foodfor a user based on user's dietary request. Physical performance datums134 may contain information such as contact information for physicalperformance executors who can fulfill and deliver a dietary request fora user. User inputs may include user inputs as to dietary request suchas for example user preferences, constitutional restrictions, and userinputs such as preference for a dietary request to arrive at user'sresidence at a certain time or a request for a certain ingredient ormeal for a dietary request. In such an instance, such data andinformation may be utilized in a k-nearest neighbors function toclassify the data points and assign a class label based on distances toknown training data vector spaces. Distances may be measured for exampleusing Euclidean distance and other algorithms such as Large MarginNearest Neighbor and/or neighborhood components analysis. Training datamay include a set of data for which class labels are known and used tocalculate a value for k, which may be calculated using heuristictechniques such as hyperparameter optimization and bootstrappingmethods. K values may then be utilized in addition to weightedcontributions of data points that are created from received dataincluding user inputs, provider datums 132, and physical performancedatums 134 to calculate and select optimal providers and physicalperformance executors.

With continued reference to FIG. 12, generation of provider instructionset 136 and/or physical performance instruction set 138 may includeidentification of one or more provider instruction sets 136 and/or oneor more physical performance instruction sets 138 as a function ofdietary request and/or alimentary instruction set 116. In an embodiment,generation of provider instruction set 136 and/or physical performanceinstruction set 138 may include identification of one or more providerinstruction sets 136 and/or one or more physical performance instructionsets 138 and insertion of the one or more instruction sets into providerinstruction set 136 and/or physical performance instruction set 138. Forexample, provider instruction set 136 and/or physical performanceinstruction set 138 may be formed, wholly or partially, by aggregatinginstruction sets and combining the aggregated instruction sets utilizingnarrative language unit 608, narrative language database 612, labelsynthesizer 604, image database 620, and/or user database 624.

With continued reference to FIG. 12, physical performance instructionset generator module 130 may generate provider instruction set 136and/or physical performance instruction set 138 by utilizing labelsynthesizer 604. Label synthesizer 604 may include any of the labelsynthesizers as described above. Label synthesizer 604 may determinethat a first provider instruction set 136 is a duplicate of a secondprovider instruction set 136 and eliminate the first providerinstruction set 136. Determination that a first provider instruction set136 is a duplicate of a second provider instruction set 136 may bedetermined for example by consulting language processing module 112and/or narrative language database 612. In an embodiment, narrativelanguage unit 608, narrative language database 612, and/or languageprocessing module 112 may ensure that information contained within bothprovider instruction set 136 and/or physical performance instruction set138 is accurate. For example, provider instruction set 136 and physicalperformance instruction set 138 may both include contact information foruser. In such an instance, language processing module 112 may ensurethat contact information is accurate and duplicate of one another so asto ensure that provider instruction set 136 contains accurate contactinformation for user while physical performance instruction set 138contains old and outdate contact information for the same user.

With continued reference to FIG. 12, physical performance instructionset generator module 130 may group information contained within providerinstruction set 136 and/or physical performance instruction set 138according to one or more classification systems relating categories ofinformation. For example, physical performance instruction set generatormodule 130 may group information into categories such as informationthat will be contained exclusively within provider instruction set 136,information that will be contained exclusively within physicalperformance instruction set 138, and information that will be sharedwill be shared and contained within provider instruction set 136 andphysical performance instruction set 138.

With continued reference to FIG. 12, physical performance instructionset generator module 130 may be configured to generate providerinstruction set 136 and/or physical performance instruction set 138 byconverting one or more alimentary instruction sets 116 into narrativelanguage. For example, physical performance instruction set generatormodule 130 may include and/or communicate with narrative language unit608, which may be configured to determine an element of narrativelanguage associated with at least an alimentary instruction set andinclude the element of narrative language in provider instruction set136 and/or physical performance instruction set 138. Narrative languageunit 608 may implement this, without limitation, by using languageprocessing module 112 to detect one or more associations betweenalimentary instruction set labels, or lists of alimentary instructionset labels, and phrases and/or statements of narrative language.Alternatively, or additionally, narrative language unit 608 may retrieveone or more elements of narrative language from narrative languagedatabase 612, which may contain one or more tables associatingalimentary instruction set labels and/or groups of alimentaryinstruction set labels with words, sentences, and/or phrases ofnarrative language. One or more elements of narrative language may beincluded in alimentary instruction set, for instance for display to auser as text describing a current alimentary instruction set status ofthe user.

With continued reference to FIG. 12, relationships between alimentaryinstruction sets, provider instruction sets, and/or physical performanceinstruction sets may be retrieved from provider instruction database1204, and/or physical performance instruction database 1208, forinstance by generating a query using one or more inputs, entering thequery, and receiving one or more categories matching the query fromprovider instruction database 1204 and/or physical performanceinstruction database 1208 as described in more detail below in referenceto FIGS. 14-15.

With continued reference to FIG. 12, physical performance instructionset generator module 130 may receive provider datums from providernetwork 144. Provider network 144 may include any of the providernetworks as described above in reference to FIG. 1. Physical performanceinstruction set generator module 130 may receive physical performancedatums 134 from physical performance entity network 146. Physicalperformance entity network 146 may include any of the physicalperformance entity networks 146 as described above in reference to FIG.1.

Referring now to FIG. 13, an exemplary embodiment of variables database1200 is illustrated. Variables database 1200 may be implemented as anydatabase and/or datastore suitable for use as dietary database 200 asdescribed above. One or more database tables in variables database 1200may include, without limitation request time 1300; request timetable1300 may include information pertaining to how far in advance a user maybe requesting a dietary input. For example, a user may generate adietary request once weekly on Sunday nights to receive a dietaryrequest with a week's worth of meals every Wednesday. In yet anothernon-limiting example, a user may generate a dietary request to receive adietary request in a shorter amount of time such as within a few merehours minutes. One or more database tables in variables database 1200may include, without limitation provider menu options 1304; providermenu options 1304 may include information pertaining to what types ofmeals and selects a user prefers from a certain provider. For example, auser may habitually order spaghetti Bolognese from a certain providerand ginger salmon with vegetables form another provider. One or moredatabase tables in variables database 1200 may include, withoutlimitation ingredient requirement table 1308; ingredient requirementtable 1308 may include information pertaining to certain ingredients mayrequire to include in a dietary request and/or require to eliminate in adietary request. Ingredient requirement may include for example certainingredients a user may require to include in a dietary request such as arequirement for a certain food group such as protein or carbohydrates,or a requirement for a certain ingredient such as avocado or wild tuna.Ingredient requirement may include for example certain ingredients auser may require to eliminate such as because of a user preference orconstitutional restriction. For example, a user with a tree nut allergymay require elimination of all tree nuts and tree nut containing fooditems. In yet another non-limiting example, a user may report aningredient requirement that includes a preference to eliminate any eggcontaining products because of an aversion to eggs. A user may report aningredient requirement such as a requirement to eliminate all glutencontaining foods and gluten containing food products because of aself-reported gluten intolerance. A user may report an ingredientrequirement such as a preference for all organic ingredients, alllocally sourced ingredients, all non-genetically modified ingredientsand the like. One or more database tables in variables database 1200 mayinclude, without limitation cost table 1312; cost table may includeinformation pertaining to cost a user is willing to spend on a dietaryrequest. Cost table may include information such as how much money auser is willing to spend on a particular meal, a particular number ofmeals, a week's worth of meals, and the like. For example, a user mayhave a total budget for a week's worth of meals that may include abreakdown by how much a user wishes to spend on a week's worth ofbreakfast, how much a user wishes to spend on a week's worth of lunch,and how much a user wishes to spend on a week's worth of dinner. One ormore database tables in variables database 1200 may include, withoutlimitation travel time table 1316; travel distance table 1316 mayinclude information pertaining to a limit on travel distance for any oneparticular performance provider executor. For example, a user who willbe receiving a meal that has been freshly prepared and served hot mayprefer a performance provider executor with a shorter travel distancethan a user who will be receiving a week's worth of frozen meals. One ormore database tables in variables database 1200 may include, withoutlimitation performance time table 1320; performance time table 1320 mayinclude information pertaining time requests generated by a user inregards to physical performance executor. For example, a user may prefera physical performance executor who can deliver a dietary request withinten minutes from a provider while another user may prefer a physicalperformance executor who can deliver a dietary request within threedays. One or more database tables in variables database 1200 mayinclude, without limitation impact on vibrant constitution table 1324;impact on vibrant constitution table 1324 may include informationpertaining to a user's particular long term health goals which may becontain dietary restraints and restrictions and current health state.For example, a user with a recent c-difficile infection and currentlytaking an antibiotic such as metronidazole may have informationcontained within impact on vibrant constitution table 1324 containingdietary restraints while on metronidazole that include zero consumptionof alcohol. In yet another non-limiting example, a user with long termhealth goal to lose body fat and increase muscle mass may haveinformation contained within impact on vibrant constitution table 1324containing dietary restraints generated by user such as a reduction incarbohydrates and an increased consumption of protein. One or moredatabase tables in variables database 1200 may include, withoutlimitation miscellaneous table 1328; miscellaneous table 1328 mayinclude miscellaneous information that may be utilized in selecting atleast a physical performance executor and/or generating at least aphysical performance instruction set. For example, a female user wholives alone may prefer a physical performance executor who is female ora user with an anaphylactic reaction to shell-fish may prefer a providerwho has a certain level of training or experience in food preparationfor individuals with anaphylactic reactions.

Referring now to FIG. 14, an exemplary embodiment of providerinstruction set database 1204 is illustrated. Provider instruction setdatabase 1204 may be implemented as any database and/or datastoresuitable for use as dietary database 200 as described above. Providerinstruction set database may be configured to be utilized by physicalperformance instruction set generator module 130 to generate providerinstruction set 136. Alternatively, or additionally, providerinstruction set database 1204 may retrieve one or more elements from anydatabase within system 100. One or more tables in provider instructionset database 1204 may include, without limitation, user table 1400; usertable 1400 may include information pertaining to a particular user. Thismay include for example, user contact information, user preferences forspecific providers, previous interactions of a user with a particularprovider, stored payment information, and user's dietary preferences.One or more tables in provide instruction set database 1204 may include,without limitation, physical performance table 1404; physicalperformance table 1404 may include information pertaining to aparticular physical performance entity and/or physical performanceexecutor. This may include for example, physical performance entityand/or physical performance executor contact information, mode oftransportation, credentials and the like. One or more tables in providerinstruction set database 1204 may include, without limitation, timetable1408; time table 1408 may include information such as time for aprovider to prepare a dietary request, hours of operation of a provider,and the like. One or more tables in provider instruction set database1204 may include, without limitation dietary request table 1412; dietaryrequest table 1412 may include information pertaining to particulardietary requests and a provider's ability to accommodate such a request.For example, dietary request table 1412 may include a list of dietaryrequests that a particular provider may prepare meals for such as glutenfree, dairy free, soy free, ketogenic, paleo, Atkins, raw foods, vegan,vegetarian, macrobiotic, and the like. One or more tables in providerinstruction set database 1204 may include, without limitation menu table1416; menu table 1416 may include information pertaining to particularmenu items a provider may prepare at any one time such as a weekly menu,daily menu, seasonal menu, menu by meal, and the like. For example, menutable 1416 may include information such as a provider's selection ofthree dinner choices on any one given night. One or more tables inprovider instruction set database 1204 may include, without limitationmiscellaneous table 1420; miscellaneous information may include anyother information that may be useful in selecting a provider and/orgenerating at least a provider instruction set.

Referring now to FIG. 15, an exemplary embodiment of physicalperformance instruction set database 1208 is illustrated. Physicalperformance instruction set database 1208 may be implemented as anydatabase and/or datastore suitable for use as dietary database 200 asdescribed above. Physical performance instruction set database 1208 maybe configured to be utilized by physical performance instruction setgenerator module 130 to generate physical performance instruction set138. Alternatively, or additionally, physical performance instructionset database 1208 may retrieve one or more elements from any databasewithin system 100. One or more tables in physical performanceinstruction set database 1208 may include, without limitation, usertable 1500; user table 1500 may include information pertaining to aparticular user. This may include for example, user contact information,user preferences for delivery within a specific amount of time, previousinteractions of a user with a physical performance executor, storedpayment information, user preferences such as location of delivery ordirections as to where a dietary request should be left at user'sresidence office location. One or more tables in physical performanceinstruction set database 1208 may include provider table 1504; providertable 1504 may include information pertaining to a particular provider.This may include for example, provider contact information, providerinstructions for physical performance executor upon arrival atprovider's location, storage and handling information pertaining to adietary request during transport, previous interactions with provider,and the like. One or more tables in physical performance instruction setdatabase 1208 may include, without limitation, time table 1508; timetable 1508 may include time of operation of a particular physicalperformance entity and/or physical performance executor. For example,time table 1508 may include particular times of operation of a physicalperformance executor. One or more tables in physical performanceinstruction set database 1208 may include, without limitation, distancetable 1512; distance table 1512 may include information pertaining tohow far a distance a particular physical performance executor may bewilling to travel. Distance may include a certain mileage distance forany one particular delivery, a certain geographical distance for any oneparticular delivery, a certain mileage distance for any one particularshift of work, a certain geographical distance for any one particularshift, and/or any other distance over a certain period of time. Forexample, a physical performance executor who works in New England mayhave a preference to deliver dietary requests within a one hundred mileradius of Nashua, N.H. In yet another non limiting example, a physicalperformance executor who resides in California may have a preference todeliver dietary requests within a thirty mile radius of executor's housebecause of heavy traffic and congestion on the roads. One or more tablesin physical performance instruction set database 1208 may includeexecutor table 1516; executor table 1516 may include informationpertaining to any one particular executor including executor contactdetails, executor identification, executor mode of transportation,executor make and model of executor mode of transportation, and thelike. For example, executor table 1516 may include details about anexecutor's car such as license plate, car color, and car model. In yetanother non-limiting example, executor table 1516 may include aparticular executor's credentials such as a captain's boating license ora train conductor's license and experience. One or more tables inphysical performance instruction set database 1208 may includemiscellaneous table 1520; miscellaneous table 1520 may include any otherinformation that may be pertinent in regard to generating physicalperformance instruction set. Miscellaneous table 1520 may includeinformation such as cost associated with different modes oftransportation such as the price for a scoter delivery versus the costfor an airplane delivery.

Referring now to FIG. 16, an exemplary embodiment of at least a providernetwork 144 and at least a physical performance network 144 isillustrated. In an embodiment, at least a provider network 144 mayinclude at least a provider 1600. At least a provider 1600 may includeany of the providers as described above. At least a provider 1600 mayexecute a provider performance. At least a provider network 144 mayinclude at least a provider server 1604. At least a provider server 1604may include any computing device suitable for use as the at least aserver 102. At least a provider network 144 may include provideinstruction set database 1204. Provider instruction set database 1204may include any of the database structures as described above inreference to FIG. 12, and FIG. 14. At least a provider network 144 maytransit and receive information from system 100, user client device 142,and/or at least a physical performance network 144. This may be doneusing any transmission methodologies including for example networktransmission as described herein. In an embodiment, system 100, userclient device 142, and/or at least a physical performance network 146may be designed and configured to interact with a plurality of providernetworks 144.

With continued reference to FIG. 16, at least a physical performancenetwork 146 may include at least a physical performance entity 1608. Atleast a physical performance network 146 may include at least a physicalperformance server 1612. At least a physical performance server 1612 mayinclude any computing device suitable for use as the at least a server102. At least a physical performance network 146 may include at least aphysical performance instruction set database 1208. At least a physicalperformance instruction set database 1208 may include any of thedatabase structures as described above in reference to FIG. 12, and FIG.15. At least a physical performance network 146 may transmit and receiveinformation from system 100, user client device 142, and/or at least aprovider network 144. This may be done using any transmissionmethodologies including for example any network transmission asdescribed herein. In an embodiment, system 100, user client device 142,and/or at least a provider network 144 may be designed and configured tointeract with a plurality of physical performance networks 146.

Referring now to FIG. 17, an exemplary embodiment of a method 1700 ofoptimizing dietary levels utilizing artificial intelligence isillustrated. At step 1705 at least a server receives at least a dietaryrequest from a user client device. The at least a server may include anyof the servers as described herein. The at least a dietary request mayinclude any of the dietary requests as described above in reference toFIGS. 1-17. At least a dietary request may include a request for aparticular diet, food, ingredient, food group, nutrition plan, style ofeating, lifestyle, and/or nutrition. At least a dietary request mayinclude a request for a particular meal, and/or a particular number ofmeals such as a week's worth of lunches or a week's worth of breakfast,lunch, and dinner. Receiving at least a dietary request may includereceiving at least a biological extraction from a user. At least abiological extraction may include any of the biological extractions asdescribed above in reference to FIGS. 1-17. Receiving at least a dietaryrequest from a user client device may include receiving at least a datumof user data including a user preference. A user preference may includeany of the user preferences as described above in reference to FIGS.1-17. A user preference may include a preference for a certain style ofeating such as a user's preference to consume a paleo diet for personalweight loss goals or a user preference for a vegetarian diet for ethicalreasons. Receiving at least a dietary request from a user client devicemay include receiving a constitutional restriction. A constitutionalrestriction may include any of the constitutional restrictions asdescribed above in reference to FIGS. 1-17. A constitutional restrictionmay include a user's self-reported intolerance to a certain food or foodgroup, such as for example a user's self-reported lactose intolerancedue to cramping and upset stomach upon consuming excess amounts of dairyproducts. A constitutional restriction may include a user'sself-reported allergy to a food or food group as previously diagnosed bya medical professional such as a functional medical doctor. For example,a user with a previously diagnosed allergy to tree nuts may self-reporta constitutional restriction to avoid all tree nuts and tree nutcontaining ingredients.

With continued reference to FIG. 17, at step 1710 the at least a servergenerates at least an alimentary instruction set as a function of the atleast a dietary request. Alimentary instruction set may include any ofthe alimentary instruction sets as described above in reference to FIGS.1-17. Generating the at least an alimentary instruction set may be doneutilizing any of the methodologies as described above in reference toFIGS. 1-17. In an embodiment, the at least an alimentary instruction setmay be generated utilizing training data. The at least a server may beconfigured to receive training data wherein receiving the training dataincludes receiving a first training set including a plurality of firstdata entries, each first data entry of the plurality of first dataentries including at least an element of dietary request data and atleast a correlated alimentary process label. In an embodiment, the atleast a server may utilize training data and the at least a dietaryrequest to generate at least a correlated alimentary process labelutilizing any of the methodologies as described above in reference toFIGS. 1-17.

With continued reference to FIG. 17, the at least a server may receiveat least a biological extraction from a user and generate the at leastan alimentary instruction set as a function of the at least a biologicalextraction. Biological extraction may include any of the biologicalextractions as described above in reference to FIGS. 1-17. Generatingthe at least an alimentary instruction set as a function of the at leasta biological extraction may include receiving a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least an element ofbiological extraction data and at least a correlated alimentary processlabel and generating the at least an alimentary instruction set as afunction of the at least a biological extraction and the training data.

With continued reference to FIG. 17, at step 1715 the at least a serverreceives at least a provider datum. At least a provider datum mayinclude any of the provider datums as described above in reference toFIGS. 1-17. At least a provider datum may include any element of datadescribing the provider, the provider's ability to prepare food for acertain dietary request, the provider's preference to prepare foodwithin a certain geographical location, and/or a menu selection of foodoptions that a provider may be able to prepare such as a weekly menu offood options. A provider datum may include for example, a provider'smenu choices for a user that may be adherent to certain diets such as amenu containing meal options that include meals that may be made glutenfree, dairy free, ketogenic, vegan, and low carbohydrate for example.

With continued reference to FIG. 17, at step 1720 the at least a serverreceives at least a physical performance datum. At least a physicalperformance datum may include any of the physical performance datums asdescribed above in reference to FIGS. 1-17. The at least a physicalperformance datum may include for example any element of data describingthe physical performance executor, the physical performance executor'sability to deliver a dietary request such as a meal based on certainconstraints such as a physical performance executor's ability to delivera dietary request such as a meal within a certain amount of time, thephysical performance executor's ability to pick up a dietary requestsuch as a meal from a provider within a certain geographical location,the physical performance executor's ability to deliver a dietary requestsuch as a meal to a user located within a certain geographical locationand the like. The at least a physical performance datum may include forexample a physical performance executor's ability to deliver a dietaryrequest within a certain geographical area or within a certain period oftime.

With continued reference to FIG. 17, at step 1725 the at least a serverselects at least a provider and at least a physical performanceexecutor. Selecting the at least a provider and the at least a physicalperformance executor may be performed using any of the methodologies asdescribed above in reference to FIGS. 1-17. Selecting the at least aprovider and the at least a physical performance executor may includegenerating a los function of user specific variables and minimizing theloss function. Loss function may include any of the loss functions asdescribed above in reference to FIGS. 1-17. User specific variables mayinclude any of the user specific variables as described above inreference to FIG. 12 and FIG. 13. User specific variables may becustomized around user specific inputs and selections. Selecting the atleast a provider and the at least a physical performance executor mayinclude producing a field of combinations of the at least a provider andthe at least a physical performance executor and selecting the at leasta provider and the at least a physical performance executor using alazy-learning learning process. Lazy-learning process may include any ofthe lazy learning processes as described above in reference to FIGS.1-17. Lazy-learning process may include for example generating ak-nearest neighbors function. K-nearest neighbors function may includeany of the k-nearest neighbors function as described above in referenceto FIGS. 1-17.

With continued reference to FIG. 17, at step 1730 the at least a servergenerates at least a provider instruction set and at least a physicalperformance instruction set as a function of the at least a providerdatum and the at least a physical performance datum and the at least analimentary instruction set. Generating the at least a providerinstruction set and generating the at least a physical performance datummay be done utilizing any of the methodologies as described above inreference to FIGS. 1-17. Generating the at least a provider instructionset and generating the at least a physical performance datum may includereceiving at least a user input datum. User input datum may include anyof the user input datums as described above in reference to FIGS. 1-17.In an embodiment, the at least a user input datum may include at least auser constraint. At least a user constraint may include any of the userconstraints as described above in reference to FIGS. 1-17. Generatingthe at least a provider instruction set and the at least a physicalperformance instruction set may include receiving at least a userconstraint, selecting at least a provider and at least a physicalperformance executor as a function of the at least constraint andtransmitting a subset of data associated with the at least a user to theat least a provider and the at least a physical performance executor.Selecting at least a provider and at least a physical performanceexecutor may include selecting as a function of fulfilling theconstraint. For example, at least a provider and/or at least a physicalperformance executor who can fulfill the user constraint may be selectedwhile at least a provider and/or at least a physical performanceexecutor who cannot fulfill the user constraint may not be selected. Inan embodiment, transmitting a subset of data associated with the usermay occur using any of the transmission methodologies as describedherein.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 18 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1800 includes a processor 1804 and a memory1808 that communicate with each other, and with other components, via abus 1812. Bus 1812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1808 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1816 (BIOS), including basic routines thathelp to transfer information between elements within computer system1800, such as during start-up, may be stored in memory 1808. Memory 1808may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1800 may also include a storage device 1824. Examples ofa storage device (e.g., storage device 1824) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1824 may beconnected to bus 1812 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1824 (or one or more components thereof) may be removably interfacedwith computer system 1800 (e.g., via an external port connector (notshown)). Particularly, storage device 1824 and an associatedmachine-readable medium 1828 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1800. In one example,software 1820 may reside, completely or partially, withinmachine-readable medium 1828. In another example, software 1820 mayreside, completely or partially, within processor 1804.

Computer system 1800 may also include an input device 1832. In oneexample, a user of computer system 1800 may enter commands and/or otherinformation into computer system 1800 via input device 1832. Examples ofan input device 1832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1832may be interfaced to bus 1812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1812, and any combinations thereof. Input device 1832may include a touch screen interface that may be a part of or separatefrom display 1836, discussed further below. Input device 1832 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1800 via storage device 1824 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1840. A networkinterface device, such as network interface device 1840, may be utilizedfor connecting computer system 1800 to one or more of a variety ofnetworks, such as network 1844, and one or more remote devices 1848connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1844, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1820, etc.) may be communicated to and/or fromcomputer system 1800 via network interface device 1840.

Computer system 1800 may further include a video display adapter 1852for communicating a displayable image to a display device, such asdisplay device 1836. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1852 and display device 1836 maybe utilized in combination with processor 1804 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1800 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1812 via a peripheral interface 1856.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for optimizing dietary levels utilizingartificial intelligence, the system comprising: at least a server,wherein the at least a server is designed and configured to: receive,from a user client device, at least a dietary request; select a trainingdata set from a plurality of training data sets, wherein the trainingdata set comprises a plurality of data entries correlating at least adietary request data to at least an alimentary process label; generate,as a function of the at least a dietary request and the training dataset, an alimentary instruction set comprising at least a suggestion ofitems to consume by a user, wherein generating the alimentaryinstruction set further comprises: training a first machine-learningmodel as a function of the training data set and a machine-learningalgorithm; and generating the alimentary instruction set as a functionof the first machine-learning model and the at least a dietary request;identify at least a meal as a function of the alimentary instructionset; and select at least a physical performance executor as a functionof the alimentary instruction set.
 2. The system of claim 1, wherein theat least a dietary request further comprises at least an element of userdata.
 3. The system of claim 1, wherein the alimentary instruction setfurther comprises at least a supplement to be consumed by a user.
 4. Thesystem of claim 1, wherein the at least a server is further configuredto generate a machine-learning algorithm, wherein the machine-learningalgorithm is configured to generate the alimentary instruction set as afunction of a classification of the at least an alimentary processlabel.
 5. The system of claim 1, wherein the at least a server isfurther configured to associate the at least a dietary request with acategory.
 6. The system of claim 5, wherein the category identifies animpactful condition.
 7. The system of claim 1, wherein the at least aserver further comprises a graphical user interface, wherein thegraphical user interface displays a plurality of meals, wherein each ofthe plurality of meals is ordered as a function of the alimentaryinstruction set.
 8. The system of claim 1, wherein the alimentaryinstruction set further comprises an element of narrative languagerelated to the alimentary instruction set.
 9. The system of claim 8,wherein the element of narrative language further comprises a textdescribing a current alimentary instruction set status of a user. 10.The system of claim 1, wherein the at least a server is furtherconfigured to generate a physical performance instruction set, whereinthe physical performance instruction set comprises a pickup location forthe at least a physical performance executor and a delivery address forthe at least a meal.
 11. A method for optimizing dietary levelsutilizing artificial intelligence, the method comprising: receiving froma user client device, at least a dietary request; selecting, by the atleast a server, a training data set from a plurality of training datasets, wherein the training data set comprises a plurality of dataentries correlating at least a dietary request data to at least analimentary process label; generating, by the at least a server, as afunction of the at least a dietary request and the training data set, analimentary instruction set comprising at least a suggestion of items toconsume by a user, wherein generating the alimentary instruction setfurther comprises: training a first machine-learning model as a functionof the training data set and a machine-learning algorithm; andgenerating the alimentary instruction set as a function of the firstmachine-learning model and the at least a dietary request; identifying,by the at least a server, at least a meal as a function of thealimentary instruction set; and selecting by the at least a server, atleast a physical performance executor as a function of the alimentaryinstruction set.
 12. The method of claim 11, wherein receiving the atleast a dietary request further comprises receiving at least an elementof user data.
 13. The method of claim 11, wherein generating thealimentary instruction set further comprises generating at least asupplement to be consumed by a user.
 14. The method of claim 11, whereingenerating the alimentary instruction further comprises generating thealimentary instruction set as a function of a classification of the atleast an alimentary label using a machine-learning algorithm.
 15. Themethod of claim 11, wherein selecting the training data further compriseassociating the at least a dietary request with a category and selectingthe training data as a function of the category.
 16. The method of claim15, wherein associating the at least a dietary request with the categoryfurther comprises identifying an impactful condition.
 17. The method ofclaim 11 further comprising displaying on a graphical user interface, aplurality of meals, wherein each of the plurality of meals is ordered asa function of the alimentary instruction set.
 18. The method of claim11, wherein generating the alimentary instruction set further comprisesgenerating an element of narrative language associated with thealimentary instruction set.
 19. The method of claim 18, wherein theelement of narrative language further comprises generating a descriptionof a current alimentary instruction set status of the user.
 20. Themethod of claim 11 further comprising generating a physical performanceinstruction set, wherein the physical performance instruction setcomprises a pickup location for the at least a physical performanceexecutor and a delivery address for the at least a meal.